Research On Digital Image Restoration And Enhancement Based On Differential

Image restoration is a key problem in image processing, and it has been the most difficult and focus question. Because of the environmental restraint and equipment physical limitations, image will suffer noise pollution and blur during image acquisition, transmission and storage. Noise and blurring will lead to image degradation. In order to get clear recovery image, lots of methods have been proposed. Recently, filtering method, regularization methods and partial differential equation (PDE) method have been quickly developed with good results. Otherwise, the result of high-density impulse noise removal or universal noise removal is not good enough. Despite high effectiveness in image deblurring and image enhancement, most of the existing algorithms tend to over smooth the image details.In this paper, the proposed methods are mainly based on differential. Firstly, mathematical model and typical methods of image restoration are overviewed. Subsequently, some new algorithms aimed to high-density impulse noise removal, universal noise removal, image deblurring and image enhancement with good edges and image details preservation are discussed respectively. The main results are given as follows:1) An efficient switching median filter aimed on high-density impulse noise removal is proposed based on Local Outlier Factor (LOF). LOF, which is popularly used in data mining, is firstly used in impulse noise detection and discrimination. LOFBDBD algorithm is designed by using boundary discriminative noise detection (BDND) combining with directional weighted median filter. LOFBDBD has high noise detection accuracy even if the noise level is above50%and provides better performance than many other median filters for noise image restoration.2) A novel method is presented for universal noise removal based on PDE. Local Difference Factor (LDF), which is computed locally from intensity values of image pixels in a neighborhood using weighted statistics, is used in noise identification. Furthermore, LDF is added in classic P-M model to control the diffusion process adaptively incorporating with local gradient. This method has great image quality by efficiently removing salt-and-pepper noise, impulse noise, Gaussian noise and mixed noise.3) A new Fractional Differential (FD) based image deblurring approach is presented. At first, the application of FD in image edge detection and image enhancement is discussed. Secondly, based on FD mask’s insensitivity to various blurring kernel, FD is adopt as an additional regularization term in image deblurring cost function to restrict the solution of the ill-posed image restoration problem. FDTV model is built by combining FD and total variation (TV) constraint. Because of comprehensive the advantages of both FD and TV in image processing, the recovery images by FDTV have good image quality, such as clear contour and edge, rich details and a certain degree of sharpness enhancement.4) A Fractional Differential (FD) based image enhancement method in multi-scale domain is presented. Firstly, the image is decomposed according to the Laplacian pyramid transform. Then, FD is adopted to enhance image pyramid levels. The enhanced image is got by image reconstruction using enhanced pyramid finally. Experimental results show that proposed image enhancement method is markedly superior to many other popular image enhancement algorithms in subjective visual effects and some objective quality assessments.