Image Enhancement by Recurrently-trained Super-resolution Network
Saem Park Nojun Kwak
eess.IV cs.CV cs.LG









Image Enhancement by Recurrently-trained Super-resolution Network
    SAEM Park

Seoul National University, Seoul, Korea

LG Eletronics, Korea

parksaem2@snu.ac.kr






Nojun Kwak

Seoul National University, Seoul, Korea

nojunk@snu.ac.kr

    December 30, 2023
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    We introduce a new learning strategy for image enhancement by recurrently training the same simple super-resolution (SR) network multiple times. After initially training an SR network by using pairs of a corrupted low resolution (LR) image and an original image, the proposed method makes use of the trained SR network to generate new high resolution (HR) images with a doubled resolution from the original uncorrupted images. Then, the new HR images are downscaled to the original resolution, which work as target images for the SR network in the next stage. The newly generated HR images by the repeatedly trained SR network show better image quality and this strategy of training LR to mimic new HR can lead to a more efficient SR network. Up to a certain point, by repeating this process multiple times, better and better images are obtained. This recurrent leaning strategy for SR can be a good solution for downsizing convolution networks and making a more efficient SR network. To measure the enhanced image quality, for the first time in this area of super-resolution and image enhancement, we use VIQET <cit.> MOS score which reflects human visual quality more accurately than the conventional MSE measure. 
    
    
    







§ INTRODUCTION




Nowadays, 2K (1920×1080) videos are widely used in digital broadband systems. From the viewpoint of image quality, there are two major sources of impaired quality in the current video images. The first one is the intrinsic limitation of image capture systems such as lens optics, sensor resolution, focusing performances and ISO noise. These defects make inherent limitations in image quality. Therefore almost every videos cannot show ideal 2K image quality, with maximum frequencies. 
The second source of corruptions is due from image processing such as compression, resolution converting and noise reduction. Most components of a broadband system have a fixed resolution and they require unavoidable needs of image scaling. Particularly, in order to transmit sources with lower resolution made in the past via a broadband system, video upscaling is inevitable. Also most of contents providers 
use heavy compression 
and restoring the lost information is a difficult task which requires enhanced techniques.







The early image enhancement techniques were mostly at the level of unsharp-masking, which is just a simple amplification operation. With the advent of high-resolution panels, super-resolution techniques have been developed to improve the original resolution. In recent years, machine learning technologies are widely used and deep learning has achieved state-of-the-art super-resolution performance. However, as the resolution of the image as well as the complexity of the algorithm increases, implementation becomes harder and harder.  
Therefore, it is necessary to develop cheaper and more effective algorithms for image enhancement and super-resolution.

In this paper, as shown in Figure <ref>, we introduce the concept of iterative learning as an approach for image enhancement. In general, if the same algorithm is applied repeatedly, the result will be reinforced. Especially in deep learning, we can approach this idea without additional computational cost at inference time. What we need is additional training with the new HR, made in the previous stage. Through this recurrent learning, the image converges to a better quality, and it is possible to obtain an enhancement over the existing super-resolution with a smaller-sized network. By training a small network with 200K parameters three repetitions, we could get images with better quality compared to a model with over 1M parameters.  



The contributions of this paper are as follows:

1. By showing an effective way of repeatedly training a simple feed-forward network for super-resolution 
through recurrent learning, we improve the efficiency of super-resolution achieving similar results to those of complex deep networks 
without much computational cost. 


2.	By shifting the evaluation criteria of the image quality from a simple arithmetic measure like Mean-Squared-Error (MSE) to a user-centered measure of VIQET <cit.> Mean-Opinion-Score (MOS), which can be computed in an automated way without human involvement, we can focus on additional subtle image quality improvement and set the number of repetitions appropriately.










§ RELATED WORK



 §.§ Image enhancement

Along with the development of computer vision and the spread of digital broadcasting, image processing techniques have been widely used in image production and reproduction. These techniques can be roughly divided into noise reduction and image enhancement. The former includes technologies such as de-noising and de-interlacing that remove artifacts that should not be present in the original image. In the latter, sharpness and contrast are used to amplify some components in the original image for better image quality. These algorithms typically perform a series of operations that widen the min-max range of the original image, such as the Laplacian filtering. The Laplacian filtering is inexpensive and effective, but it is not possible to perform more complex processing such as restoring missing texture components or broken edges. 

There are several image enhancement algorithms that resolve these weaknesses. The first one is improving the adaptiveness of the processing region such as Adaptive Unsharp Masking <cit.>, which selects the processing region wisely. The second one is to use two or more weights in the function to enable more complex processing, which includes Bilateral Filter <cit.> and NLM Filter<cit.>. There are huge amount of related works, but with the advent of super-resolution, research on conventional image enhancement has diminished.
This is mainly because the improvement is due from the amplification of some components, and the original frequency is maintained. Ultimately, it requires an approach such as super-resolution in order to change the shape of the original. We present a direction for developing new super-resolution images for better quality.



 §.§ Super-resolution

Before the boom of deep learning, as a better upscaler, super-resolution algorithms were developed. These techniques have mainly been applied to a display system such as a television. It is proposed as a solution to compensate for the mismatch between the input image size and the panel resolution. 
There are two main categories of this technology: 1) utilizing machine learning algorithms and 2) weighted image blending using the patches from the original image. The former is similar to the super-resolution approaches through the current deep learning, but with simple architectures consisting of only one layer. The latter is a self-image generation method that retrieves the most similar patches from the surrounding areas of images after upscaling <cit.>. 
Conventional super-resolution methods are well documented in <cit.>. They can perform upscaling while keeping the sharpness of an edge. However, there are technical limitations such as the problem for a large upscaling ratio or impossibility of restoring complex textures.  These require huge computations, but it was difficult to learn enough with a human-designed shallow network. Recently, deep neural networks have been improved to enable more powerful super-resolution processing. This is why we use deep learning as a solution.




 §.§ Super-resolution with deep learning

Recently, as a branch of deep learning research, `single image super-resolution' has emerged. It makes down-sized low-resolution (LR) images and trains convolution networks so as to reproduce the original images from the LR images. 
Compared to the revolutionarily simple SRCNN <cit.>, the architecture has become heavier and more complex. 
VDSR <cit.> introduced VGG and ResNet into the super-resolution filed in 2015. ESPCN <cit.> and DRCN <cit.> present various aspects of residual structure and the view of upscaling in 2016. SRGAN <cit.>, SRResNET <cit.>, DRRN <cit.>, EDSR <cit.>, DenseSR <cit.>, MDSR <cit.> and Memnet <cit.> developed Resnet architecture and adjusted the GAN solution into super-resolution in 2017. In 2018, super-resolution is now more widely used in terms of utilization. 
A good example is  <cit.>. There is a research utilizing a recursive method for super-resolution <cit.>. 
Different from ours, 
it tries to produce a face image from an 8×8 tiled mosaic 
through repeated application of super-resolution, which is similar to info-GAN <cit.> that produces a facial image.


Regardless of the super-resolution algorithm, 
the general operation principle is similar. Basically, the single image super-resolution is learned by back propagation so as to reduce the difference between the original and the generated image through the SR network from an LR image. 


There are some limitations in these works. First, in the low-resolution images used in other SR papers, the improvement of the image quality by increasing the resolution is remarkable. However in a high-resolution image, the improvement caused by an increase in resolution is not obvious. Second, the advances in super-resolution have led to an increased performance sacrificing the computational complexity. 
This high computational demand makes it hard to implement super-resolution with hardware. Therefore, it is necessary to develop a method that can achieve better performance with a more simple network. Finally, since current super-resolution algorithms are learned only from the original images, they target the original and we cannot expect image quality beyond the original. In the following section, we present a new approach to get images with better quality at a low cost through recurrent learning.




§ PROPOSED METHOD




 §.§ Recurrent Training Strategy (RTS)

The core of this paper is the recurrent training strategy of an SR network which is achieved by reducing the size of the output image of the SR network to its original size and retraining the SR network with this new target. The proposed recurrent training strategy is briefly shown in Figure <ref>.




As can be seen in the figure, a stage of RTS consists of two phases. In the first phase (Phase A), SR network is trained. In this phase, the SR is learned so that the target image is obtained from the degraded LR image, and the weight of the super-resolution network is updated. The LR image is a low quality image which is corrupted through down-scaling, adjusting compression and so on. 
In Stage 1, the target is set to the original image, HR_0 and the network is tuned such that the LR is improved to the image quality of the original HR_0 level. 

The second phase, Phase B, is the process of creating a new target image using the network obtained in Phase A. This is done by inputting the original image HR_0 to the trained network of Phase A and we can expect that the output image SR_1 has higher quality than the original image HR_0. By downscaling the output image SR_1, we can obtain a new image HR_1 which acts as a target in the next stage.   
Generally, super-resolution is accompanied by increasing the size of the image to increase the image resolution. So the additional down-scaling process is required because the resolution must be the same as the original for retraining the network. 


To summarize, we have to go through two steps to get a new HR image. The former is the process of obtaining the super-resolution function SR(·), and the latter is the process of obtaining enhanced image by down-scaling the output image of the SR network back to the original size. Then, we perform this process repeatedly on a stage basis, so that the same super-resolution processing can be more effective. 
The overall procedure of Stage n can be summarized as follows:

    Phase A:  SR_n = _SR1/2||HR_n-1 - SR(LR)||^2 
       Phase B:  HR_n = d(SR_n(HR_0)).

Here, SR(·) and d(·) denote the super-resolution network and the down-scaling operation, respectively. LR is an image which has lower resolution than the original HR_0. 


By applying super-resolution to the original image, a higher quality image is expected to be  obtained. Even if the obtained image is down-scaled to the original size, components from the additionally generated resolution may remain, this is the same as an UHD (Untra HD) channel that looks better than the existing channels on a FHD (full HD) TV. 
If HR_n has a better image quality than HR_n-1, we can assume that the super-resolution network can learn from this better target, and after the learning, the resultant network will be better. 
Therefore, assuming that we go through this additional process in a direction that gradually improves the results, we will eventually reach the maximum improvement point of each image. And it will perform better than the current super-resolution network trained to mimic the original image.









 §.§ Reason for enhanced quality

If y is the original image, U is the up-scaler, D is the down-scaler, and R is the residual component, the super resolution trained by x=D(y) can be expressed as follows: 

    SR(x) = U(x) + R(x),


    SR(D(y)) = U(D(y)) + R(D(y)).

Here, we can suppose D(U(y)) is the same as y, and U(D(y)) becomes a locally blurred version of y: 

    D(U(y)) = y,   
        U(D(y)) = y̅


Combining (<ref>) and (<ref>), we get 

    R(x) = SR(x) - y̅.

Because the goal of learning is to minimize the error ||y-SR(x)||:

    SR^* = _SR( 1/2 y - SR(x) ^2),

if enough learning has been done, R(x) becomes similar to y-y̅:

    R(x) ≈ y - y̅.

In the first stage, after the super resolution is fully learned, instead of D(y), we can input y to the SR network, then it becomes

    SR_1(y) = U(y) + R_1(y).

By down-scaling this, we get the target image for stage 2 which can be expressed as

    HR_1(y) = D(SR_1(y)) = D(U(y) + R_1(y)).

Assuming a linear[For a nonlinear down-scaler such as nearest neighbor or bicubic interpolation, we can take Taylor series expansion.] down-scaler, as D(U(y)) = y, it becomes 

    HR_1(y) = y + D(R_1(y)).

The distributions of zero-mean residual components of different sized images will be approximately equal, that is p(z|z∈ R_1(x)) = p(z|z∈ R_1(y)), unless there are not much high frequency components only existing in y and not in x. By down-scaling R_1(y), the distribution will be more contracted near zero and we can approximate D(R_1(y)) with a contracted version of R_1(x) as 




    D(R_1(y)) ≈α R_1(x) ,    ( 0 ≤α≤ 1 ).

Finally, with (<ref>), HR_1(y) can be approximated as

    HR_1(y) ≈ y + α R_1(x) ≈ y + α (y-y̅).





The above equation has high similarity with unsharp masking. In other words, HR_1(y) has a high probability that the components with a large deviation from the mean are amplified. And it can be expected that the new target image HR_1(y) can have improved sharpness compared to y.





When the super resolution network is trained with this new target, it becomes 

    HR_2(y) ≈ y + α R_2(x) ≈ y + α(y-y̅) + α^2 (y-y̅).

This is obtained by replacing y in (<ref>) with the new target y+α(y-y̅) in computing R_2(x).
As stage goes on, it becomes 





    HR_n(y) ≈ y + ∑_i=1^n α^i (y-y̅).


Since α is smaller than 1, (Experimental results show that the average α for stage 1 on DIV2K valid set is 0.779 with the standard deviation of 0.098.) higher order α terms will disappear and HR_n can be expected to convergence to a certain point ŷ not far from y. 
Using the SR network, the actual difference y-y̅ is learned by a nonlinear function and it makes hard to guarantee convergence, these non-linearity has much better performance than the simple USM algorithm. As iteration goes on, the difference tend to converges to ŷ-y̅ and the image quality of ŷ becomes better than that of the original image y.





 §.§ Configuration of super-resolution network

We set up a convolution network based on the residual layer. The use of multiple layers of residual type has the advantage of automatically adapting image filters of various sizes. In order to make use of these advantages and to easily control the number of parameters, we constructed the network shown in Fig. <ref>. 

This network is designed exclusively for recurrent learning, which uses a subsequent down-scaler after the operation of super-resolution. The biggest difference from the regular super resolution network is that it produces the best output of the same resolution with original, not in an upscaled domain. Learning proceeds in the same way as the existing super resolution, but since the expected output is synthesized in 3ch at the original resolution, optimum result is produced at the corresponding step.

It is composed of an input module, an output module and three 64-channel residual networks using 3×3 filters 
The input module consists of 128×128 RGB (3ch) input and 64ch output. After the input module, a series of residual layers, all of which have the same number of channels (64 ch) with 3×3 convolutions, is applied. As shown in the figure, the base model uses 3 layers. In the output module, the final residual layer does not make use of ReLU because the difference between the input and the output will have both a positive and negative value. Here, the output with blue residual will be next stage's HR, because the next channel extension and pixel shuffle has no ReLU and it can be canceled by the down-scaler. The residual layer is based on SRResNet <cit.> and VDSR <cit.>. The final residual without ReLU is from VDSR, and the inner residual layer shares the concept with SRResNet.

Finally, we need a down-scaler to convert the up-scaled image to the same size as the original, using the Lanczos scaler. Lanczos filter has better performance than linear and bicubic filters, and it makes α greater in (<ref>).












 §.§ Setting the number of stages


In our proposal, the number of stages N can be set as the point where the quality of HR_N+1 is worse than that of HR_N and the corresponding network SR_N can be used as the final super resolution network. 

The important thing here is how to judge whether the output image gets better or not. As a result of our experiment, we can consider a couple of solutions. The easiest approach is to measure the quality of the actual validation set to see if this score is increased. This is intuitive, but it has the disadvantage that it is difficult to use it because of the time and effort required for measurement. The second method is to confirm whether the change ||HR_N+1 - HR_N|| becomes larger or smaller as the repetition progresses. As the output of each stage approaches a certain ideal point, the difference will gradually decrease. When the output of the stage deviates from the ideal point, the divergence starts and the difference will increase. This will be explained in more detail in the following experimental results.










§ EXPERIMENTAL RESULT





 §.§ Training setup

In our experiment, we used the DIV2K image dataset <cit.>, which contains 2K RGB images. 
This is to prove the performance of image enhancement on contents in the FHD level which occupies the majority of current broadcasting contents. The DIV-2K dataset consists of 800 train sets and 100 validation sets. From the 800 DIV-2K training set, we made our training database which was constructed by ripping 100 random patches with a 256×256 size. After making the original images, HR_0, we adjusted the JPEG compression and down-scaled the patches to the half size to create the LR images. The learning was carried out from the 80,000 pairs made in this way.



In all of our experiments, we used MSE as a loss function and Adam as an optimizer. Batch size of 8 was used with learning rate of 0.001. We trained the network for 20 epochs.






 §.§ Convergence of recurrent learning

In order to confirm whether the proposed recurrent learning converges to the maximum improvement point or simply diverges as if the Laplacian filter is applied multiple times, we observed the difference of output images between consecutive stages. The image difference is an objective indicator that shows the average difference of all the pixels in an image in terms of ratio. It can be similar to MSE, but it is normalized to the image's brightness and resolution. It is a more interpretable measure in that it is more intuitive telling the percentage of the image  changed. The average image difference of the 100 validation images in each stage is shown in Table <ref>.





Figure <ref> shows Table <ref> to a graph. It shows that the learning converges to a certain point. And it can be seen that the change of difference starts to increase after a specific point, the Stage 4.
In order to confirm that the recurrent learning is different from the repetitive application of a simple filter, we repeatedly applied unsharp-masking (USM) filter to the original image, which is shown by the green line. The difference increases linearly and image difference from Stage (N-1) does not converge ever. Also, from the observation that the image difference is linearly increased by repeated USM application, we can confirm that the image difference is properly measured.







 §.§ The result images

Figure <ref> shows some samples of the output images in each stage. It shows how the resultant images for each stages are being improved. All images are down-scaled with the same resolution as the original, and we can visually confirm that the quality of the images is improved. The improvement level at each stage is similar to the slope of the graph in Fig. <ref>. As stage goes on, it can be observed that the texture is clearly seen and the result becomes clearer. 
It can be said that recurrent learning converges to a certain level of improvement, since there is little difference between HR_3 and HR_4 in all the samples.





 §.§ Verification using VIQET tool

In order to clearly demonstrate the effectiveness of image enhancement, we looked for ways to more objectively measure the image quality. Many non-reference quality measure models exists, but most of them need pre-learning the references and check how close the measured image is from the learned. This is not what we want, because we do not have any reference. Fortunately, the Video Quality Experts Group (VQEG) has developed a tool that measures the image quality and developed the Mean Opinion Score (MOS) of an image as 0 (Poor) to 4.5 (Best). Using this tool, we can evaluate the image quality improvement for each stage objectively.

In the VQEG image quality Evaluation Tool (VIQET) <cit.>, the average MOS score is derived for each image through a total of 11 measures: 
1. Resolution, 2. Multi-scale Edge Acutance, 3. Noise Signature Index, 4. Flat Region Index, 5. Multi-scale Texture Acutance, 6. Saturation, 7. Color Warmth, 8. Illumination, 9. Dynamic Range, 10. Over Exposed ratio, 11. Under Exposed ratio. To use VIQET, we must set a category of an image among 4 categories: 
Outdoor Day - Landscape, Indoor - Wall Hanging, Indoor - Arrangements
 and 
Outdoor Night - Landmark. 
Most of the DIV-2K images are bright images taken from the outside and we simply set all the images to the outdoor day category. The detailed average scores of 100 DIV-2K validation images for each stage are shown in Table <ref>. 

The key point is that the overall score, MOS value, increases with the stage. The result proves that “The image quality can be increased by recurrent learning”. The MOS results for each stage are shown in Fig. <ref>. Based on the MOS, it can be confirmed that the image quality from the original 3.825 was improved up to 4.057. The increase is 0.232 points from the original, which is larger than the difference between LR and the original, 0.180. It prove that image enhancement through recurrent learning is efficient. There is a limitation in improving the image quality based on the difference between HR_0 and LR with one-time learning and we have resolved this by providing a new target obtained through recurrently training the network. 
As can be seen in Fig. <ref>, the MOS increase is largely due to the improvement of four terms. The first one is the improvement of multi-scale edge acutance value. It means the edge got to be more sharp. The second is the Noise Signature Index, which means that the distinction between image and noise is more clear. The third one is multi-scale texture acutance, and it means increment of the level of activity and detail in the scene. Lastly, illumination means the light has become more enriched, also indicating that the image has more detail.









 §.§ RTS on various networks


To investigate the characteristics of the proposed RTS more clearly and to check whether the proposed RTS is still effective with other algorithms, we test RTS on several other networks 
as well as the proposed network.
RTS was applied to total six networks and the performances are shown in Table <ref>. In the table, the above three networks are designed based on our proposal shown in Fig. <ref> by changing the number of residual layers in the middle to 1, 3 and 6, respectively. 
The proposed architecture in Fig. <ref> has the advantage of being able to easily compare the number of parameters in the network considering the number of repetitions.  For example, a comparison between learning once with doubled parameters and learning twice with halved parameters is possible. In the three bottom rows,  we have chosen SRCNN, VSDR, SRResNet and ESDR to check whether our recurrent training strategy is meaningful even in a heavier super-resolution network. By this experiment, the characteristics of the proposed RTS on various networks can be checked from the simplest network, SRCNN, to a latest heavy network, EDSR.

Unfortunately, the existing networks are so heavy that the GPU memory overflows, making it impossible to keep the original size of a DIV2K image intact. So we have to divide each image into 4×2 and processed them independently. The MOS result is the averaged value of 800 pieces of divided 100 validation images. The segmentation of the image caused a decrease in the content and resolution, which resulted in a decrease in the MOS score. However, since this segmentation is applied equally to all the six networks, the tendency is comparable and the experiment is fair. Table <ref> shows the resultant MOS scores as well as the number of parameters in each network. 

In Table <ref>, the scores of HR2 and HR3 are higher than HR1 at all times. This suggests that recurrent learning may lead to additional performance improvement in existing algorithms. In addition, it is shown that the improvement due from the learning method is more meaningful than the improvement caused by using a different network. It means that the quality of an image is more dependent on the number of repetitions in RTS than the size of the network, and as a result, the proposed RTS can produce good results even with light networks.


Although the amount of increase in MOS is different for each algorithm, the tendency of learning is similar. From this, we can say that applying RTS makes it possible to use a less expensive network.









§ CONCLUSION


In this paper, we proposed a new image enhancement method by a recurrently-trained super-resolution network. 
The proposed recurrent learning consists of two phases: Network Training phase to learn HR form LR, and Target Update phase to apply the trained network to the original to produce HR image for the next stage. We clarified the characteristics of recurrent learning by analyzing the process and results. Using image difference, we can show the results converge to a specific improvement point without divergence. Also, by using VIQET mean-opinion-score, it has been numerically shown that the quality of an image improves clearly as we repeat the learning procedure. 

It is the first time to use these objective measures in the image-enhancement area, which is meaningful in that the MOS score is useful not only in a theory <cit.> but also in practice. 

The DIV2K image dataset used in this paper has an equivalent image quality to that of an actual digital broadcasting video, which is different from the conventional datasets with low quality used in the existing super-resolution papers. Nonetheless, by RTS, the MOS score increased more than the difference between the original and its low-resolution version, which is impossible with the conventional one-time learning. The increased MOS score proves that image enhancement through recurrent learning is efficient.

In order to clarify the effectiveness of the proposed RTS, we experimented with various networks. The results show that recurrent learning can make an additional improvement, regardless of the  network used. And the results show a way to obtain a cheap but effective network. We expect that it will contribute to the related society by dramatically reducing the cost of super-resolution and image enhancement.

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