International Journal of Advanced Network, Monitoring and Controls | 2021
Motion Blur Image Restoration by Multi-Scale Residual Neural Network
Abstract
Blind deblurring is a basic subject of computer vision and image processing. Motion image deblurring is divided into non blind deblurring and blind deblurring by whether to estimate the blur kernel. Blind deblurring is easy to produce motion artifacts because of the inaccurate estimation of the blur kernel. Non blind deblurring is the best choice for the current blurred image processing. The purpose of this paper is to further improve the definition of blurred image, restore the edge information of contour, and strengthen the repair of texture details. Based on the multi-scale convolution neural network, a multi-scale residual network is proposed, which can comprehensively extract image features, enhance image feature fusion, and constrain image generation by combining multi-scale loss function with anti loss function. The performance of the algorithm is evaluated by testing the peak signal to noise ratio (PSNR) structure similarity and restoration time of the generated image relative to the clear image. This algorithm improves the average PSNR on GOPRO testset, and reduces the recovery time accordingly. It can successfully recover the detail information lost due to motion blur. This algorithm has simple network structure, strong robustness and good restoration effect, and is suitable for dealing with various image degradation problems caused by motion blur. Keywords-Image Deblur; Motion Deblur; Multi-Scale Residual Network; Deep Learning I. THE BACKGROUND OF DEBLURRING Humans rely on the visual system to obtain a large amount of information. Studies have shown that about 70% of the information is obtained through the visual system. Therefore, the acquisition, processing and use of image information is particularly important. From the exploration of space 60 years ago, the importance of image restoration technology can be seen. At that time, the images sent back to the earth from space were affected by the imaging technology at that time, the shooting environment was not ideal, the relative movement between objects and the camera shake [1]. And other problems, resulting in degradation of the returned image, such as low image resolution, blurred image, etc. In order to solve the problem of image degradation caused by various reasons, people began to study image restoration algorithms. The two most typical image degradation phenomena are noise and blur. In the process of acquiring images, many factors can cause poor image quality, such as object movement and solar radiation. Alignment is out of focus, optical deviation, atmospheric flow, etc. In the process of image transmission, the image will also be blurred and noise due to the interference of the transmission channel and the shooting of electronic components. These degraded images bring great difficulties for subsequent image processing, such as image feature extraction, target object tracking and other tasks. With the International Journal of Advanced Network, Monitoring and Controls Volume 06, No.01, 2021 58 widespread use of images in various fields, people are also pursuing higher and higher resolution of images to deblur. Therefore, it is necessary to continuously research on image restoration technology to meet human visual requirements and applications in various fields. There are three main types of blur, Gaussian blur, defocus blur and motion blur. There are three types of blur: Gaussian blur, defocus blur and motion blur. Gaussian blur is caused by the Gaussian distribution of each pixel in the image, which is formed by the external diffusion and superposition. The center image is more blurred, and the edge image is more loose. Defocus blur is caused by different depth of field in the process of photographing, some or all of the objects are not in the plane of the imaging system, and there will be local or global defocus blur in the image. The defocus blur is mainly caused by the camera focusing inaccuracy, which leads to different degrees of degradation of objects in different depths of the image [2]. Defocus blur is similar to a disk, and the influence gradually decreases from the center to the outside. In the process of motion blur shooting, the relative displacement between the camera and the object is caused by the motion blur, which is called motion blur. Motion blur can be solved by two methods, one is to reduce the noise exposure time, which can reduce the phenomenon of image motion blur, but with the decrease of exposure time, the signal-to-noise ratio of the image will decrease, and the quality of the image will also decline. The second is to simulate the gradient distribution of the image through mathematical modeling, and further study the image deblurring The research object of this paper is motion blur, which is the image blur caused by lens out of focus, object movement, camera shake and other factors dublurring the shooting process[3].For motion blur, equipment can be avoided by using a sports camera. However, such equipment is generally expensive, ten times or even dozens of times the price of ordinary cameras, and it is difficult to popularize and use it on a large scale. Therefore, using efficient and convenient algorithms designed to restore clear images from motion blurred images is currently the mainstream method to deal with motion blurred. Motion blur is based on the image blur mechanism to model, solve and restore the corresponding high-quality clear image. When the fuzzy kernel is unknown, deblurring is a typical ill-posed problem. How to obtain the final clear image of the image with few known variables has brought many difficulties to the research. With the continuous improvement of mathematica. theoretical knowledge and the rapid development of computer vision technology, motion deblurring has made great progress and development, and is widely used in astronomical detection, traffic monitoring, industrial detection, target detection and other fields. With the continuous growth of demand and the ever-changing blurring scenes in real applications, this puts forward higher requirements for image deblurring technology, and at the same time brings greater challenges. Image deblurring is an important classification of image restoration technology, and it is also the current research field of computer vision activities [4]. It has important research significance and application value. Image restoration mainly focuses on two aspects. One is to reduce or avoid the blur of the captured image by improving the hardware equipment. The main implementation method is to build the corresponding control system to achieve the purpose of stable shooting, or stable imaging equipment, this method can effectively control the image blur, but it will increase the cost of imaging difficulty. The second is to process the image after imaging, that is, to achieve the purpose of blurred image restoration through the research of image motion blur removal algorithm. Image deblurring is a serious ill posed problem, because in the process of solving, due to the interference of unknown fuzzy kernel and noise and other factors, the difficulty of image motion blur algorithm is also increasing. Therefore, the image motion blur removal algorithm still needs continuous research and improvement [5]. According to the nature of the blur kernel, it is divided into blind deblurring and non-blind deblurring. Non-blind deblurring results in artifacts in the image due to the deviation of the blur kernel estimation, and can only restore limited image blur. Blind deblurring does not rely on the estimation of the blur kernel and achieves International Journal of Advanced Network, Monitoring and Controls Volume 06, No.01, 2021 59 end-to-end deblurring, but due to the illposed nature of blind deblurring, the details of the image are missing, Enhance the color saturation of the image to meet human visual needs. Therefore, this article will focus on restoring the contour edges of the image. A multi-scale residual module is added to the network, and different convolution kernel sizes are used to extract more image features through the information sharing of the shallow network and the deep network. Based on the inspiration of DeblurGAN [6], the method of combining the counter loss function and the multi-scale function is adopted to adjust the network parameters and train a stable network to achieve the research purpose. II. RELATED INFORMATION There are many causes of image blurring. It may be affected by the resolution of the capture device, lighting conditions, atmospheric motion, and the photographer s shooting level, etc., resulting in different degrees and types of blurring in the captured pictures. According to the different types of blur, blurred images can be divided into motion blur, defocus blur, Gaussian blur and so on. This article mainly analyzes the image degradation model of motion blur. Motion blur is the blur produced by the relative displacement between the device and the shooting object during the exposure time of the shooting device. There are many uncontrollable factors that cause image motion blur, such as sun exposure, camera shake, atmospheric movement, and so on. Motion blur image restoration can be widely used in various fields, traffic monitoring, medical imaging, target detection, etc. Therefore, restoring clear images is a hot spot in the field of computer vision today.. The degradation model of motion blur is shown below, b is a blurred image, k is a clear image, and l is a blur kernel, also called a point spread function. The blur kernel is a kind of convolution kernel. This convolution kernel will make the image produce special effects, n is additive noise. The research of this paper does not estimate the fuzzy kernel l, and directly outputs clear images from end to end. \uf020 * b k l n \uf03d \uf02b \uf020 \uf028\uf031\uf029\uf020 Through the mathematical modeling and analysis of the motion blur image, the motion blur removal is to establish a corresponding mathematical model, ex