or damaged due to some external factors like lens wrong positioning , transmission interference , low lighting or motion blurs and etc.. which is referred to as noise. when the images are degraded or damaged the information to be extracted from that also gets damaged. therefore we need to recover or restore the image as it was intended to be. the aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images. the simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. more sophisticated methods assume a model of how the local image structures look, to distinguish them from noise. by first analyzing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches. an example in this field is inpainting. system methods the organization of a computer vision system is highly application-dependent. some systems are stand-alone applications that solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc. the specific implementation of a computer vision system also depends on whether its functionality is pre-specified or if some part of it can be learned or modified during operation. many functions are unique to the application. there are, however, typical functions that are found in many computer vision systems. image acquisition – a digital image is produced by one or several image sensors, which, besides various types of light-sensitive cameras, include range sensors, tomography devices, radar, ultra-sonic cameras, etc. depending on the type of sensor, the resulting image data is an ordinary 2d image, a 3d volume, or an image sequence. the pixel values typically correspond to light intensity in one or several spectral bands (gray images or colour images), but can also be related to various physical measures, such as depth, absorption or reflectance of sonic or electromagnetic waves, or nuclear magnetic resonance. pre-processing – before a computer vision method can be applied to image data in order to extract some specific piece of information, it is usually necessary to process the data in order to assure that it satisfies certain assumptions implied by the method. examples are: re-sampling to assure that the image coordinate system is correct. noise reduction to assure that sensor noise does not introduce false information. contrast enhancement to assure that relevant information can be detected. scale space representation to enhance image structures at locally appropriate scales. feature extraction – image features at various levels of complexity are extracted from the image data. typical examples of such features are: lines, edges and ridges. localized interest points such as corners, blobs or points. more complex features may be related to texture, shape or motion. detection/segmentation – at some point in the processing a decision is made about which image points or regions of the image are relevant for further processing. examples are: selection of a specific set of interest points. segmentation of one or multiple image regions that contain a specific object of interest. segmentation of image into nested scene architecture comprising foreground, object groups, single objects or salient object parts (also referred to as spatial-taxon scene hierarchy), while the visual salience is often implemented as spatial and temporal attention. segmentation or co-segmentation of one or multiple videos into a series of per-frame foreground masks, while maintaining its temporal semantic continuity. high-level processing – at this step the input is typically a small set of data, for example a set of points or an image region which is assumed to contain a specific object. the remaining processing deals with, for example: verification that the data satisfy model-based and application-specific assumptions. estimation of application-specific parameters, such as object pose or object size. image recognition – classifying a detected object into different categories. image registration – comparing and combining two different views of the same object. decision making making the final decision required for the application, for example: pass/fail on automatic inspection applications. match/no-match in recognition applications. flag for further human review in medical, military, security and recognition applications. image-understanding systems image-understanding systems (ius) include three levels of abstraction as follows: low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events. many of these requirements are entirely topics for further research. the representational requirements in the designing of ius for these levels are: representation of prototypical concepts, concept organization, spatial knowledge, temporal knowledge, scaling, and description by comparison and differentiation. while inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing. inference and control requirements for ius are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction. hardware an 2020 model ipad pro with a lidar sensor there are many kinds of computer vision systems; however, all of them contain these basic elements: a power source, at least one image acquisition device (camera, ccd, etc.), a processor, and control and communication cables or some kind of wireless interconnection mechanism. in addition, a practical vision system contains software, as well as a display in order to monitor the system. vision systems for inner spaces, as most industrial ones, contain an illumination system and may be placed in a controlled environment. furthermore, a completed system includes many accessories such as camera supports, cables and connectors. most computer vision systems use visible-light cameras passively viewing a scene at frame rates of at most 60 frames per second (usually far slower). a few computer vision systems use image-acquisition hardware with active illumination or something other than visible light or both, such as structured-light 3d scanners, thermographic cameras, hyperspectral imagers, radar imaging, lidar scanners, magnetic resonance images, side-scan sonar, synthetic aperture sonar, etc. such hardware captures "images" that are then processed often using the same computer vision algorithms used to process visible-light images. while traditional broadcast and consumer video systems operate at a rate of 30 frames per second, advances in digital signal processing and consumer graphics hardware has made high-speed image acquisition, processing, and display possible for real-time systems on the order of hundreds to thousands of frames per second. for applications in robotics, fast, real-time video systems are critically important and often can simplify the processing needed for certain algorithms. when combined with a high-speed projector, fast image acquisition allows 3d measurement and feature tracking to be realized. egocentric vision systems are composed of a wearable camera that automatically take pictures from a first-person perspective. as of 2016, vision processing units are emerging as a new class of processor, to complement cpus and graphics processing units (gpus) in this role.