Image enhancement can improve the perception of information for human viewers , but there also exist some deficiencies in most image enhancement methods . This paper analyzes the deficiencies and gives a solution to improve image enhancement results-image fusion . Image enhancement is an important function of stereoscopic endoscopy . 3D images of stereoscopic endoscopy usually suffer from various kinds of degradation because of undesired noise , inhomogeneous brightness and poor contrast . The image enhancement method can be used to effectively remove this adverse effect , but the assessment of the enhancement effect is a subject which needs strong theoretical and practical support . Aiming at the common Gaussian noise and Rayleigh noise in remote sensing images , the traditional method of image enhancement processing is used , and the subjective and objective evaluation methods are combined to compare the advantages and disadvantages of the different image enhancement methods . The results provide a good idea for the evaluation of the remote sensing image processing methods and quality . However , how to improve the efficiency and effectiveness of digital image processing for the surface profile has become a main topic of current research . By experimental analysis of the parallel situation of multiprocessor systems in digital image processing , aparallel implementation method for image preprocessing and image enhancement processing is proposed in this paper , which makes multiple processors work more coordinately and makes full use of the CPU , thus greatly improving the performance of the multiprocessor computer system and the efficiency and effectiveness of the image processing . In this study the authors will make the process of image enhancement segmented image enhancement mikrofilm . This research was intented to image enhancement process through several steps : Negative Digital Image Inversion ( ND ) into Positive Digital ( PD ) mode , Then , the Possitive Digital was convoluted using Unsharp Mask Filter ( UMF ) operation . The result will be analyzed using two analytical methods , first based on respondent subjective analysis and professionals who has high concern on photography and mikrofilm . The method is better applied to monitoring service and applies a weight-based luminance and gray level block linear expansion image enhancement algorithm into a DVR ( digital video recorder ) screenshot processing method . Before a screenshot file is generated , the weight-based luminance and gray level block linear expansion image enhancement algorithm is used for image enhancement operation for original image data so as to improve image quality , and then the image data subjected to image enhancement operation are stored to an image file . The DVR screenshot processing method includes : acquiring the original image data ; making statistics on image luminance and gray level extreme values ; judging by inputting image luminance and gray level threshold values ; inputting image luminance and gray level blocks ; inputting image block luminance and gray level statistics ; outputting image luminance and gray level blocks ; performing weight-based luminance and gray level block linear expansion ; and storing processed image data . The gray level image is mapped into fuzzy domain using a novel linear membership function . Then , the generalized fuzzy enhancement operator is applied for the image enhancement operation . The enhancement result is calculated by using the method of linear mapping . Because lateral inhibition involves only near neighbor interactions between cells , optoelectronic implementations in the form of monolithically integrated photodetector arrays are particularly attractive . These have wide practical applicability for machine vision , optical scanning , and image enhancement purposes . Such implementations are completely analog and can be designed to require only one transistor per unilateral interconnection.<<ETX > > This chapter contains sections titled : Overview of Image Representation Image Processing with BF533/BF537 EZ-KIT Color Conversion Color Conversion with BF533/BF537 EZ-KIT Two-Dimensional Discrete Cosine Transform Two-Dimensional DCT/IDCT with BF533/BF537 EZ-KIT Two-Dimensional Filtering Two-Dimensional Filtering with BF533/BF537 EZ-KIT Image Enhancement Image Enhancement with BF533/BF537 EZ-KIT Image Processing with LabVIEW Embedded Module for Blackfin Processors More Application Projects The use of Gaussian filter shell reduced the MSE to a larger extent from the enhanced image . 1.1 Image Enhancement Image enhancement is a process of improving the quality of image by improving its features . The techniques used for image enhancement can be used to improve the image 's contrast and brightness characteristics and reduces its noise contents or sharpen its details they can be classified as subjective enhancement and objective enhancement ( 9 ) . Traditionally , image QA algorithms interpret image quality as fidelity or similarity with a " reference " or " perfect " image . We apply the VIF method on image enhancement effect which takes distorted image as " reference " image instead of " perfect " image to assess the quality of enhanced image . It provides clear advantages over the traditional approaches because VIF index is combined with HVS features under certain conditions . Image enhancement processes increase the visual information of that image . Global Histogram Equalization ( GHE ) , Local histogram equalization , Brightness preserving Dynamic Histogram equalization and adaptive based histogram equalization ( AHE ) are several method of image process enhancement . Histogram based techniques are used to enhance contrast of all type of images , but low contrast image analysis is a challenging problem and it has main constraint of efficiency of this method . This dissertation proposes the use of a Cellular Neural Network to model , detect and classify objects appearing in multiple object scenes . The algorithm proposed is based on image scene enhancement through anisotropic diffusion ; object detection and extraction through binary edge detection and boundary tracing ; and object classification through genetically optimised associative networks and texture histograms . The first classification method is based on optimizing the space-invariant feedback template of the zero-input network through genetic operators , while the second method is based on computing diffusion filtered and modified histograms for object classes to generate decision boundaries that can be used to classify the objects . document : Image Restoration Using Convolutional Auto - encoders with Symmetric Skip Connections Image restoration , including image denoising , super resolution , inpainting , and so on , is a well - studied problem in computer vision and image processing , as well as a test bed for low - level image modeling algorithms . In this work , we propose a very deep fully convolutional auto - encoder network for image restoration , which is a encoding - decoding framework with symmetric convolutional - deconvolutional layers . The weights are learned by minimizing the difference between the output and the clean image . By observing recent superior performance of CNN on image processing tasks , here we propose a very deep fully convolutional CNN - based framework for image restoration . The input of our framework is a corrupted image , and the output is its clean version . Convolution Neural Networks : CNN is a feedforward neural network with 2D convolution layers and 2D pooling layers , originally developed for image processing . Then CNN is applied to NLP tasks , such as sentence classification , and relation classification . layer typically discards some pixel - wise information . For image restoration problems such as super - resolution and denoising , image details are very important . Therefore , most deep - learning approaches for such problems do not use pooling . It can not handle the imbalance of easy and hard images in the dataset . In our paper , we propose to exploit hard example mining on image level , hard image mining , to improve the quality of face detector on extremely hard faces . More specifically , we assign difficulty scores to training images while training with an SGD mechanism , and re - sample the training images to build a new training subset at the next epoch . The overall structure of the proposed DSRN is shown in Figure [ reference ] , which not only utilizes parameters efficiently but also allows both LR and HR signals to contribute jointly to learning the mappings . To demonstrate the effectiveness of the proposed method , we compare DSRN with other recent image SR approaches on four common benchmarks as well as on the DIV2 K dataset from the " New Trends in Image Restoration and Enhancement workshop and challenge on image super - resolution ( NTIRE SR 2017 ) " . Extensive experimental results validate that DSRN delivers higher parameter efficiency , low memory consumption and high restoration accuracy . Dynamic range compression is an important function used in modern digital video cameras and displays to improve visual quality of standard dynamic range color images . This chapter presents a real-time implementation of an adaptive contrast-enhancing image dynamic range compression algorithm on a graphics processing unit ( GPU ) for color image enhancement . To achieve this purpose , an image-dependent nonlinear intensity transfer function is first presented to produce a satisfactory dynamic-range compression result with less color artifacts . The image 's mean luminance can be adjusted as desired by adjusting the asymmetricity between the gain control factors of the two maps . The model performs local contrast enhancement in the contrast domain of an image where it lends itself very naturally to such adjustments . Furthermore , the model is extended on to colour images using the concept of colour-opponent receptive fields found in the human visual system . In this paper , we propose a novel approach for the detection of traffic signs , which is well-suitable to different light conditions , change of scale and rotation . The detection process has four stages : image color enhancement , phase symmetry computation , morphological filter and maximally stable extremal regions ( MSERs ) detection phase . We combine the color information of traffic signs using color enhancement with the symmetry of its shape type , and performing phase symmetry computation to highlight the Region of interest ( ROI ) . Automatic color enhancement are aimed to automaticly and adaptively adjust photos to expected styles and tones . For current learned methods in this field , global harmonious perception and local details are hard to be well-considered in a single model simultaneously . Color enhancement is a very important aspect of photo editing . Even when photographers have tens of or hundreds of photographs , they must enhance each photo one by one by manually tweaking sliders in software such as brightness and contrast , because automatic color enhancement is not always satisfactory for them . To support this repetitive manual task , we present self-reinforcing color enhancement , where the system implicitly and progressively learns the user 's preferences by training on their photo editing history . While enhancing the intensity or saturation component for high-quality color enhancement , keeping the hue component unchanged is important ; thus , perceptual color models such as HSI and HSV were used . Hue-Saturation-Intensity ( HSI ) is a public color model , and many color applications are commonly based on this model . The proposed method can be applied to some image processing tasks that require pixel-wise manipulations , where deep RL has never been applied . We apply the proposed method to three image processing tasks : image denoising , image restoration , and local color enhancement . Our experimental results demonstrate that the proposed method achieves comparable or better performance , compared with the state-of-the-art methods based on supervised learning . In addition , because the operations executed at each pixels are visualized , we can change or modify the operations if necessary . We apply the proposed method to a variety of image processing tasks : image denoising , image restoration , local color enhancement , and saliency-driven image editing . Our experimental results demonstrate that the proposed method achieves comparable or better performance , compared with the state-of-the-art methods based on supervised learning . Color image enhancement is a very important pre-processing stage in face detection and face recognition applications especially when the environment is very dark . In this paper , a color color image enhancement with the adaptive filter and discrete wavelet transform ( DWT ) is proposed . Discrete wavelet transform is used to improve image enhancement and stationary wavelet transform is used to reduce the halo distortion in gray scale images only and wavelet can not work in color images . Salient regions with a color similar to that of their vicinity are detected successively through agglomerative clustering . The algorithm introduces novel methods used in color saliency enhancement into the context of local feature detection . The color saliency enhancement approach is evaluated by detecting salient objects . In this paper a non-sub-sampled shearlet transform model for image enhancement to improve image quality by maximizing the information content in the input image is proposed and implemented . This is inspired by two basic human visual features : i ) visual adaptation , ii ) local contrast enhancement of color image . We achieve the goal in different stages : In initial stage evolved upon the shearlet transformation of an image from image space to a low-pass and high-pass images in shearlet space and then to the visual response space . The hardware system structure of the imaging system mainly consists of a SCMOS image sensor , a programmable gate array ( FPGA ) , a driving circuit of a combination of DDR2 and a Camera Link transfer circuit . The software system consists of SCMOS initialization module , automatic exposure control module and image enhancement module , which has the capabilities of automatic exposure control and low-light image enhancement . Automatic exposure control module is proposed based on the image gray statistical algorithm . Images shared on the Internet are often compressed into a small size , and thus have the JPEG artifact . This issue becomes challenging for task of low-light image enhancement , as the artifacts hidden in dark image regions can be further boosted by traditional enhancement models . We use a divide-and-conquer strategy to tackle this problem . With a conservative use of best computational operators , the proposed architecture achieves state-of-the-art accuracy with 30X less model parameters and 70X faster inference time for color constancy . It is also demonstrated that the proposed architecture achieves similar efficiency in case of other low-level image restoration problems such as Low-Light enhancement . In this work , we aim to shift the focus towards a more functional direction , that is enhancement that prioritizes feature retrieval . For this reason , we first propose to model low-light enhancement as a set of localized functions using Gaussian Process ( GP ) that is trained at runtime using data from a simple Convolutional Neural Network ( CNN ) to provide the necessary feature information as reference . The CNN is in turn trained using large amount of synthetic data , based upon the luminance distribution of real world low-light images to learn the relationship between features and pixels . Low light image enhancement is prerequisite in many fields , such as surveillance systems , road safety and waterway transport . In this paper , a new variational framework using bright channel prior is proposed to address the low light image enhancement problem within a single image . An alternating direction optimization method is employed to solve the variational problem . Deep learning-based methods have achieved remarkable success in image restoration and enhancement , but are they still competitive when there is a lack of paired training data ? As one such example , this paper explores the low-light image enhancement problem , where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene . We propose a highly effective unsupervised generative adversarial network , dubbed EnlightenGAN , that can be trained without low/normal-light image pairs , yet proves to generalize very well on various real-world test images . Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods . In addition , the proposed method can be extended for dehazing , sandstorm removal and low illumination image enhancement , which can illustrate better capacity of our model . Images captured in low illumination conditions usually suffer from a poor visibility which has an important affect on the performance of computer vision systems . Thereby low illumination image enhancement is critical for image-related applications . In this paper , we proposed a photographic negative imaging inspired method for enhancing the images taken under low illumination environments . Noise causes unpleasant visual effects in low-light image/video enhancement . In this paper , we aim to make the enhancement model and method aware of noise in the whole process . Low-Light Image/Video Enhancement Using CNNs . We present a deep learning based method for low-light image enhancement . Low- light Image enhancement is a fundamental problem in computer vision . Over the years , many efforts have been invested in improving the quality of images captured under low- lightning conditions . Enhancement algorithms can make low light level images have a clear visual effect like the one captured during the daytime , but due to high complexity and generous computational cost , low light level image enhancement algorithms are usually difficult to meet real-time requirements which make it difficult to be widely used in practical application . For this situation , a parallel optimization algorithm of low light level image enhancement using CUDA is proposed . Enhancement algorithm based on de-hazing technique is used and on CPU-GPU heterogeneous platform the part of atmospheric light estimation which is not suitable for parallel computing is improved to obtain high parallelism degree .
Image enhancement can improve the perception of information for human viewers , but there also exist some deficiencies in most image enhancement methods . This paper analyzes the deficiencies and gives a solution to improve image enhancement results-image fusion .
Image enhancement is an important function of stereoscopic endoscopy . 3D images of stereoscopic endoscopy usually suffer from various kinds of degradation because of undesired noise , inhomogeneous brightness and poor contrast .
The image enhancement method can be used to effectively remove this adverse effect , but the assessment of the enhancement effect is a subject which needs strong theoretical and practical support . Aiming at the common Gaussian noise and Rayleigh noise in remote sensing images , the traditional method of image enhancement processing is used , and the subjective and objective evaluation methods are combined to compare the advantages and disadvantages of the different image enhancement methods . The results provide a good idea for the evaluation of the remote sensing image processing methods and quality .
However , how to improve the efficiency and effectiveness of digital image processing for the surface profile has become a main topic of current research . By experimental analysis of the parallel situation of multiprocessor systems in digital image processing , aparallel implementation method for image preprocessing and image enhancement processing is proposed in this paper , which makes multiple processors work more coordinately and makes full use of the CPU , thus greatly improving the performance of the multiprocessor computer system and the efficiency and effectiveness of the image processing .
In this study the authors will make the process of image enhancement segmented image enhancement mikrofilm . This research was intented to image enhancement process through several steps : Negative Digital Image Inversion ( ND ) into Positive Digital ( PD ) mode , Then , the Possitive Digital was convoluted using Unsharp Mask Filter ( UMF ) operation . The result will be analyzed using two analytical methods , first based on respondent subjective analysis and professionals who has high concern on photography and mikrofilm .
The method is better applied to monitoring service and applies a weight-based luminance and gray level block linear expansion image enhancement algorithm into a DVR ( digital video recorder ) screenshot processing method . Before a screenshot file is generated , the weight-based luminance and gray level block linear expansion image enhancement algorithm is used for image enhancement operation for original image data so as to improve image quality , and then the image data subjected to image enhancement operation are stored to an image file . The DVR screenshot processing method includes : acquiring the original image data ; making statistics on image luminance and gray level extreme values ; judging by inputting image luminance and gray level threshold values ; inputting image luminance and gray level blocks ; inputting image block luminance and gray level statistics ; outputting image luminance and gray level blocks ; performing weight-based luminance and gray level block linear expansion ; and storing processed image data .
The gray level image is mapped into fuzzy domain using a novel linear membership function . Then , the generalized fuzzy enhancement operator is applied for the image enhancement operation . The enhancement result is calculated by using the method of linear mapping .
Because lateral inhibition involves only near neighbor interactions between cells , optoelectronic implementations in the form of monolithically integrated photodetector arrays are particularly attractive . These have wide practical applicability for machine vision , optical scanning , and image enhancement purposes . Such implementations are completely analog and can be designed to require only one transistor per unilateral interconnection.<<ETX > >
This chapter contains sections titled : Overview of Image Representation Image Processing with BF533/BF537 EZ-KIT Color Conversion Color Conversion with BF533/BF537 EZ-KIT Two-Dimensional Discrete Cosine Transform Two-Dimensional DCT/IDCT with BF533/BF537 EZ-KIT Two-Dimensional Filtering Two-Dimensional Filtering with BF533/BF537 EZ-KIT Image Enhancement Image Enhancement with BF533/BF537 EZ-KIT Image Processing with LabVIEW Embedded Module for Blackfin Processors More Application Projects
The use of Gaussian filter shell reduced the MSE to a larger extent from the enhanced image . 1.1 Image Enhancement Image enhancement is a process of improving the quality of image by improving its features . The techniques used for image enhancement can be used to improve the image 's contrast and brightness characteristics and reduces its noise contents or sharpen its details they can be classified as subjective enhancement and objective enhancement ( 9 ) .
Traditionally , image QA algorithms interpret image quality as fidelity or similarity with a " reference " or " perfect " image . We apply the VIF method on image enhancement effect which takes distorted image as " reference " image instead of " perfect " image to assess the quality of enhanced image . It provides clear advantages over the traditional approaches because VIF index is combined with HVS features under certain conditions .
Image enhancement processes increase the visual information of that image . Global Histogram Equalization ( GHE ) , Local histogram equalization , Brightness preserving Dynamic Histogram equalization and adaptive based histogram equalization ( AHE ) are several method of image process enhancement . Histogram based techniques are used to enhance contrast of all type of images , but low contrast image analysis is a challenging problem and it has main constraint of efficiency of this method .
This dissertation proposes the use of a Cellular Neural Network to model , detect and classify objects appearing in multiple object scenes . The algorithm proposed is based on image scene enhancement through anisotropic diffusion ; object detection and extraction through binary edge detection and boundary tracing ; and object classification through genetically optimised associative networks and texture histograms . The first classification method is based on optimizing the space-invariant feedback template of the zero-input network through genetic operators , while the second method is based on computing diffusion filtered and modified histograms for object classes to generate decision boundaries that can be used to classify the objects .
document : Image Restoration Using Convolutional Auto - encoders with Symmetric Skip Connections Image restoration , including image denoising , super resolution , inpainting , and so on , is a well - studied problem in computer vision and image processing , as well as a test bed for low - level image modeling algorithms . In this work , we propose a very deep fully convolutional auto - encoder network for image restoration , which is a encoding - decoding framework with symmetric convolutional - deconvolutional layers .
The weights are learned by minimizing the difference between the output and the clean image . By observing recent superior performance of CNN on image processing tasks , here we propose a very deep fully convolutional CNN - based framework for image restoration . The input of our framework is a corrupted image , and the output is its clean version .
Convolution Neural Networks : CNN is a feedforward neural network with 2D convolution layers and 2D pooling layers , originally developed for image processing . Then CNN is applied to NLP tasks , such as sentence classification , and relation classification .
layer typically discards some pixel - wise information . For image restoration problems such as super - resolution and denoising , image details are very important . Therefore , most deep - learning approaches for such problems do not use pooling .
It can not handle the imbalance of easy and hard images in the dataset . In our paper , we propose to exploit hard example mining on image level , hard image mining , to improve the quality of face detector on extremely hard faces . More specifically , we assign difficulty scores to training images while training with an SGD mechanism , and re - sample the training images to build a new training subset at the next epoch .
The overall structure of the proposed DSRN is shown in Figure [ reference ] , which not only utilizes parameters efficiently but also allows both LR and HR signals to contribute jointly to learning the mappings . To demonstrate the effectiveness of the proposed method , we compare DSRN with other recent image SR approaches on four common benchmarks as well as on the DIV2 K dataset from the " New Trends in Image Restoration and Enhancement workshop and challenge on image super - resolution ( NTIRE SR 2017 ) " . Extensive experimental results validate that DSRN delivers higher parameter efficiency , low memory consumption and high restoration accuracy .
Dynamic range compression is an important function used in modern digital video cameras and displays to improve visual quality of standard dynamic range color images . This chapter presents a real-time implementation of an adaptive contrast-enhancing image dynamic range compression algorithm on a graphics processing unit ( GPU ) for color image enhancement . To achieve this purpose , an image-dependent nonlinear intensity transfer function is first presented to produce a satisfactory dynamic-range compression result with less color artifacts .
The image 's mean luminance can be adjusted as desired by adjusting the asymmetricity between the gain control factors of the two maps . The model performs local contrast enhancement in the contrast domain of an image where it lends itself very naturally to such adjustments . Furthermore , the model is extended on to colour images using the concept of colour-opponent receptive fields found in the human visual system .
In this paper , we propose a novel approach for the detection of traffic signs , which is well-suitable to different light conditions , change of scale and rotation . The detection process has four stages : image color enhancement , phase symmetry computation , morphological filter and maximally stable extremal regions ( MSERs ) detection phase . We combine the color information of traffic signs using color enhancement with the symmetry of its shape type , and performing phase symmetry computation to highlight the Region of interest ( ROI ) .
Automatic color enhancement are aimed to automaticly and adaptively adjust photos to expected styles and tones . For current learned methods in this field , global harmonious perception and local details are hard to be well-considered in a single model simultaneously .
Color enhancement is a very important aspect of photo editing . Even when photographers have tens of or hundreds of photographs , they must enhance each photo one by one by manually tweaking sliders in software such as brightness and contrast , because automatic color enhancement is not always satisfactory for them . To support this repetitive manual task , we present self-reinforcing color enhancement , where the system implicitly and progressively learns the user 's preferences by training on their photo editing history .
While enhancing the intensity or saturation component for high-quality color enhancement , keeping the hue component unchanged is important ; thus , perceptual color models such as HSI and HSV were used . Hue-Saturation-Intensity ( HSI ) is a public color model , and many color applications are commonly based on this model .
The proposed method can be applied to some image processing tasks that require pixel-wise manipulations , where deep RL has never been applied . We apply the proposed method to three image processing tasks : image denoising , image restoration , and local color enhancement . Our experimental results demonstrate that the proposed method achieves comparable or better performance , compared with the state-of-the-art methods based on supervised learning .
In addition , because the operations executed at each pixels are visualized , we can change or modify the operations if necessary . We apply the proposed method to a variety of image processing tasks : image denoising , image restoration , local color enhancement , and saliency-driven image editing . Our experimental results demonstrate that the proposed method achieves comparable or better performance , compared with the state-of-the-art methods based on supervised learning .
Color image enhancement is a very important pre-processing stage in face detection and face recognition applications especially when the environment is very dark . In this paper , a color color image enhancement with the adaptive filter and discrete wavelet transform ( DWT ) is proposed . Discrete wavelet transform is used to improve image enhancement and stationary wavelet transform is used to reduce the halo distortion in gray scale images only and wavelet can not work in color images .
Salient regions with a color similar to that of their vicinity are detected successively through agglomerative clustering . The algorithm introduces novel methods used in color saliency enhancement into the context of local feature detection . The color saliency enhancement approach is evaluated by detecting salient objects .
In this paper a non-sub-sampled shearlet transform model for image enhancement to improve image quality by maximizing the information content in the input image is proposed and implemented . This is inspired by two basic human visual features : i ) visual adaptation , ii ) local contrast enhancement of color image . We achieve the goal in different stages : In initial stage evolved upon the shearlet transformation of an image from image space to a low-pass and high-pass images in shearlet space and then to the visual response space .
The hardware system structure of the imaging system mainly consists of a SCMOS image sensor , a programmable gate array ( FPGA ) , a driving circuit of a combination of DDR2 and a Camera Link transfer circuit . The software system consists of SCMOS initialization module , automatic exposure control module and image enhancement module , which has the capabilities of automatic exposure control and low-light image enhancement . Automatic exposure control module is proposed based on the image gray statistical algorithm .
Images shared on the Internet are often compressed into a small size , and thus have the JPEG artifact . This issue becomes challenging for task of low-light image enhancement , as the artifacts hidden in dark image regions can be further boosted by traditional enhancement models . We use a divide-and-conquer strategy to tackle this problem .
With a conservative use of best computational operators , the proposed architecture achieves state-of-the-art accuracy with 30X less model parameters and 70X faster inference time for color constancy . It is also demonstrated that the proposed architecture achieves similar efficiency in case of other low-level image restoration problems such as Low-Light enhancement .
In this work , we aim to shift the focus towards a more functional direction , that is enhancement that prioritizes feature retrieval . For this reason , we first propose to model low-light enhancement as a set of localized functions using Gaussian Process ( GP ) that is trained at runtime using data from a simple Convolutional Neural Network ( CNN ) to provide the necessary feature information as reference . The CNN is in turn trained using large amount of synthetic data , based upon the luminance distribution of real world low-light images to learn the relationship between features and pixels .
Low light image enhancement is prerequisite in many fields , such as surveillance systems , road safety and waterway transport . In this paper , a new variational framework using bright channel prior is proposed to address the low light image enhancement problem within a single image . An alternating direction optimization method is employed to solve the variational problem .
Deep learning-based methods have achieved remarkable success in image restoration and enhancement , but are they still competitive when there is a lack of paired training data ? As one such example , this paper explores the low-light image enhancement problem , where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene . We propose a highly effective unsupervised generative adversarial network , dubbed EnlightenGAN , that can be trained without low/normal-light image pairs , yet proves to generalize very well on various real-world test images .
Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods . In addition , the proposed method can be extended for dehazing , sandstorm removal and low illumination image enhancement , which can illustrate better capacity of our model .
Images captured in low illumination conditions usually suffer from a poor visibility which has an important affect on the performance of computer vision systems . Thereby low illumination image enhancement is critical for image-related applications . In this paper , we proposed a photographic negative imaging inspired method for enhancing the images taken under low illumination environments .
Noise causes unpleasant visual effects in low-light image/video enhancement . In this paper , we aim to make the enhancement model and method aware of noise in the whole process .
Low-Light Image/Video Enhancement Using CNNs . We present a deep learning based method for low-light image enhancement .
Low- light Image enhancement is a fundamental problem in computer vision . Over the years , many efforts have been invested in improving the quality of images captured under low- lightning conditions .
Enhancement algorithms can make low light level images have a clear visual effect like the one captured during the daytime , but due to high complexity and generous computational cost , low light level image enhancement algorithms are usually difficult to meet real-time requirements which make it difficult to be widely used in practical application . For this situation , a parallel optimization algorithm of low light level image enhancement using CUDA is proposed . Enhancement algorithm based on de-hazing technique is used and on CPU-GPU heterogeneous platform the part of atmospheric light estimation which is not suitable for parallel computing is improved to obtain high parallelism degree .
A solution to the deficiencies of image enhancement Image enhancement based on contrast limited adaptive histogram equalization for 3D images of stereoscopic endoscopy Research on Quality Assessment Methods of Remote Sensing Image Parallelization Study of Curved Surface Contour Image Processing in a Multi-processor System PERBAIKAN KUALITAS CITRA HASIL INVERSI DENGAN MENGGUNAKAN ALGORITMA UNSHARP MASKING FILTER (UMF) PADA KOLEKSI MIKROFILM NEGATIF MUSEUM SONOBUDOYO YOGYAKARTA Method for improving screenshot quality of digital video recorder Improved Fast Image Enhancement Algorithm Based on Fuzzy Set Theory Sensory neural networks and their adaptations Practical DSP Applications: Digital Image Processing Performance Evaluation of Filters for Enhancement of Images in Different Application Areas An image enhancement effect objective assessment method based on visual information fidelity Histogram based Contrast Image Enhancement Overview Modeling multiple object scenarios for feature recognition and classification using cellular neural networks Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling Deeply-Recursive Convolutional Network for Image Super-Resolution Robust Face Detection via Learning Small Faces on Hard Images Image Super-Resolution via Dual-State Recurrent Networks A GPU-Accelerated Adaptive Simultaneous Dynamic Range Compression and Local Contrast Enhancement Algorithm for Real-Time Color Image Enhancement Biologically Inspired Contrast Enhancement Using Asymmetric Gain Control Research on Road Sign Detection Method Based on Phase Symmetry and MSER A Coarse-to-Fine Framework for Learned Color Enhancement with Non-Local Attention SelPh: Progressive Learning and Support of Manual Photo Color Enhancement Color image enhancement in HSI color space without gamut problem Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing PixelRL: Fully Convolutional Network With Reinforcement Learning for Image Processing Traditional Color Image Enhancement Based on Adaptive Filter Detection of saliency maximally stable color regions Automatic contrast enhancement of low-light images based on Naka-Rushton visual adaptation in non-sub-sampled shearlet domain A low light SCMOS imaging system based on FPGA Enhancing Low-Light Images with JPEG Artifact Based on Image Decomposition Decoupling Semantic Context and Color Correlation with Multi-class Cross Branch Regularization Low-light image enhancement using Gaussian Process for features retrieval A variational framework for single low light image enhancement using bright channel prior EnlightenGAN: Deep Light Enhancement without Paired Supervision Underwater Image Enhancement with a Total Generalized Variation Illumination Prior A photographic negative imaging inspired method for low illumination night-time image enhancement LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model MBLLEN: Low-Light Image/Video Enhancement Using CNNs Image Enhancement in Varying Light Conditions Based on Wavelet Transform Parallel implementation of low light level image enhancement using CUDA
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ICIA Embedded Signal Processing with the Micro Signal Architecture ArXiv COLING 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition DICTA CHI ISCCSP AAAI ISSPIT PCM (2) GlobalSIP ISPA/BDCloud/SocialCom/SustainCom BMVC ICIA
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