Fuzzy image restoration method

Image Restoration-Fuzzy Image Processing Solution

Machine vision intelligent detection 20 17-06- 16

There are many reasons for image blurring, and the blurred images caused by different reasons need different methods to deal with them. Technically, fuzzy image processing methods are mainly divided into three categories, namely image enhancement, image restoration and super-resolution reconstruction. This paper will analyze from these three aspects.

Intelligent equipment management technology is to use the equipment management service of system management platform software to continuously monitor all monitoring equipment in real time, including cameras, ptz, encoders and system servers. When a fault is found, it can alarm in various ways in time, prompting maintenance personnel to deal with it in time. A system can deploy multiple equipment management servers according to the network topology, and inspect the equipment in real time by region, which can greatly improve the maintenance efficiency of the system, and try to get monitoring and alarm within 10 minutes when the equipment fails.

Construction goal

This scheme intends to apply advanced machine learning and computer vision technology to simulate human visual system, and make accurate judgments on common camera failures such as snowflake, screen scrolling, blur, color cast, frozen picture, unbalanced gain, out-of-control pan/tilt, and illegal acts of malicious shielding and destruction of monitoring equipment, and automatically record all detection results and generate reports. Users can conveniently maintain the urban public safety image resource system.

Technology roadmap

There are eight kinds of video faults: video signal loss, abnormal video definition, abnormal video brightness, video noise, video snowflake, video color cast, picture freezing and PTZ motion out of control. In which the video signal is lost. With the large-scale construction of "Safe City", a large number of video surveillance systems have been built in major cities. Although the monitoring system has been widely used in banks, shopping malls, stations, traffic intersections and other public places, in public security work, due to the limitation of equipment or other conditions, the image playback after the incident is unclear and the data is incomplete, which cannot provide effective clues for the timely detection of the case. There are often problems such as unclear facial features of suspects and illegible license plates of suspected vehicles. It has brought great trouble to the public security department to solve the case and the court to collect evidence. With the promotion of safe cities and the further promotion of various monitoring systems in various places, such problems will become more and more prominent.

Causes of image blur

There are many reasons for image blurring, such as inaccurate focusing, aberration of optical system, relative motion during imaging, atmospheric turbulence effect, low illumination, random environmental noise and so on. In addition, the encoding, decoding and transmission of images may lead to further blurring of images. Generally speaking, the main reasons for image blur are as follows:

Improper lens focusing, camera failure, etc. ;

Transmission is too far, video cable is aging, environmental electromagnetic interference, etc.

The camera shielding window or lens is dirty and blocked.

Serious environmental impacts, such as fog, dust, rain and snow;

Blur caused by video compression algorithm and transmission bandwidth;

Low camera resolution and undersampling imaging;

The limit resolution of optical lens and the blur caused by camera mismatch;

Motion blur caused by moving objects in high-speed motion;

……

Common Solutions to Blurred Images

For fuzzy image processing technology, domestic universities and scientific research institutions have been studying these theories and applications for many years, and have published many related documents, and achieved some good applications. Cognitech software in the United States is a set of quite mature fuzzy image restoration application software, which has been used in law enforcement agencies such as the Federal Bureau of Investigation for many years. The restored image can be directly used as court evidence, which shows that the fuzzy image processing technology has achieved considerable practical application.

As mentioned above, there are many reasons for image blurring, and to achieve better processing results, different reasons often require different processing methods. Technically, fuzzy image processing methods are mainly divided into three categories, namely image enhancement, image restoration and super-resolution reconstruction.

Image enhancement

Many traditional image algorithms can reduce image blur, such as image filtering, geometric transformation, contrast stretching, histogram equalization, spatial sharpening, brightness homogenization, morphology, color processing and so on. Individually, these algorithms are relatively mature and simple. However, for specific blurred images, one or more of the above algorithms often combine different parameters to achieve ideal results. The combination of these algorithms and parameters has further developed into specific enhancement algorithms, such as "image defogging" algorithm, "image denoising" algorithm, "image sharpening" algorithm, "image dark detail enhancement" algorithm and so on. These algorithms greatly improve the clarity and quality of images.

Image defogging algorithm can be realized by comprehensive application of morphology, image filtering and color processing. Figure 1 is the practical effect of a defogging algorithm, and there are many similar image enhancement algorithms, so I won't list them one by one.

Image restoration

Image restoration, like image enhancement technology, is also a technology to improve image quality. Image restoration is to establish a degradation model according to the prior knowledge of image degradation, and then based on this model, adopt various inverse degradation processing methods to restore it step by step, thus improving image quality.

There is a difference between image restoration and image enhancement, both of which are aimed at improving image quality. Image enhancement does not consider how the image degrades. Only by exploring various technologies can the visual effect of the image be enhanced, while image restoration is completely different. We need to know the prior knowledge of image degradation process and find the corresponding inverse process method according to it, so as to get a clear restored image. The accuracy of image restoration mainly depends on the prior knowledge of image degradation process.

The method of image restoration is effective for image blur caused by defocus, motion and atmospheric turbulence. Commonly used algorithms include Wiener filtering algorithm, wavelet algorithm and training-based method. Fig. 3 is an example of using Wiener filter to solve the motion blurred image, which has achieved good restoration effect. Knowing the degradation model, image restoration can achieve better results than image enhancement.

Image super-resolution reconstruction

The main goal of the existing monitoring system is to monitor the macro scene. A camera covers a large area, which makes the target in the picture too small for human eyes to directly identify. The blur caused by this undersampling accounts for a large proportion, and super-resolution reconstruction method is needed for the blur caused by undersampling.

Super-resolution restoration is a signal processing method to improve image resolution and image quality. Its core idea is to improve the resolution of the image by estimating the high-frequency components of the signal outside the cutoff frequency of the imaging system. Super-resolution restoration technology only deals with a single image at first. This method has inherent limitations in image restoration effect, because the available information is only a single image. The super-resolution restoration technology of sequence images aims at processing the degraded low-resolution images in the sequence through signal processing methods to obtain one or more high-resolution restored images. Because the restoration of sequence images can make use of extra information between frames, it is better than the restoration of single frame, and it is a research hotspot at present.

Super-resolution restoration of sequence images is mainly divided into two categories: frequency domain method and spatial domain method. The frequency domain method has the advantages of simple theory and low computational complexity, but the disadvantage is that it is limited to global translational motion and linear space invariant degradation model, and its ability to contain spatial prior knowledge is limited. The observation model used in spatial method involves global and local motion, spatially variable fuzzy point spread function, non-ideal sub-sampling and so on. , and has a strong ability to contain spatial transcendental constraints. Commonly used spatial methods include non-uniform interpolation, iterative back projection (IBP), convex set projection (POCS), maximum a posteriori estimation (MAP), maximum likelihood estimation (ML), filtering and so on. Among them, there are many researches on maps and POCS, and there is a large room for development. The specific algorithm is not the focus of this paper, so I won't introduce it in detail here. Fig. 5 is an example of super-resolution reconstruction using multi-frame low-resolution images.

Key and deficiency of fuzzy image processing technology

Although many fuzzy image processing methods have achieved good results in practical application, there are still some factors that restrict the further development of fuzzy image processing, mainly in the following aspects.

The algorithm is highly targeted.

Most fuzzy image processing algorithms are only suitable for specific images, and the algorithms themselves cannot intelligently decide whether an algorithm module is turned on or off. For example, for foggy images, "defogging algorithm" can achieve a good processing effect, but when it is applied to normal images, the image effect will decrease, and the opening or closing of "defogging algorithm" module requires manual intervention.

Algorithm parameter complexity

All algorithms in fuzzy image processing will contain a large number of parameters, and the selection of these parameters needs to be combined with the actual image performance, which directly determines the final processing effect. As far as the current algorithm is concerned, there is no way to intelligently choose which parameters are optimal.

Empirical of algorithm flow.

Because the actual image is very complex, it needs to deal with many situations, which requires an algorithm processing flow. For a specific blurred video, it is difficult to automatically choose what processing flow to adopt, and it is necessary to manually choose the appropriate method, which can only rely on human experience.

label

Due to the influence of environment, lines, lenses and cameras, some videos will be blurred after the monitoring system is built and operated for a period of time.

Generally speaking, although the fuzzy image processing algorithm has been widely used, the image algorithm is limited, and all problems cannot be pinned on the image algorithm. Different types of fuzzy problems should be treated differently. With the help of video diagnosis system, lens defocusing, dust occlusion, line aging, camera failure and other causes blur or image quality degradation. , must be repaired in time to solve the problem from the source. For low illumination, day and night high-sensitivity cameras are preferred. For image quality degradation caused by rain, fog, motion and undersampling, various fuzzy image processing algorithms included in the Video Enhancement Server can be used to improve image quality.

People who like this content also like it.

The website 17 commonly used by teachers is recommended to you, so you don't have to look for resources everywhere anymore.

The website 17 commonly used by teachers is recommended to you, so you don't have to look for resources everywhere anymore. ...

University teacher service studio

dislike

The reason for not watching

of course

The content quality is low, don't look at this official WeChat account.

What is terrazzo? By the designer to a new height.

What is terrazzo? By the designer to a new height. ...

Alliance design library

dislike

The reason for not watching

of course

The content quality is low, don't look at this official WeChat account.