DEBLURRING AND DENOISING OF IMAGE USING DIFFERENT TECHNIQUES

Abstract

Low-rank matrix approximation has been successfully used in various image processing problems like image deblurring, image denoising etc. Previously we studied about low rank prior of similar patches for image deblurring by combining the properties of the blurry image and its gradient map. In order to get better kernel estimation, we employed the weighted nuclear norm minimization method which further enhanced the e ectiveness of low rank prior by eliminating the inconsiderable edges and ne texture in intermediate images and preserve the dominant edges of the image. Here we performed both the quantitative and qualitative analysis for both uniform and non-uniform image deblurring. We further extend this method of low rank prior using WNNM to jointly perform image denoising and image deblurring. In this, we show that how to produce a high-quality image by combining the extracted information from both the denoised image and deblurred image which cannot be obtained by simply denoising the noisy image or deblurring the blurred image alone. In this method, we also consider the role residual noise left in the image even after iteratively denoising the image. In order to further enhance the denoising process i.e. texture and edges of the image, we utilize the dissimilarity between the di erent singular values of image patches and then perform image deblurring in order to obtain the clean image. We compare the result with existing methods and show the improvements achieved both qualitatively and quantitatively using our method. In the end, we also implement the dark channel prior technique for both uniform and non-uniform image deblurring and further extend this method of dark channel prior for image denoising and compare this method with di erent image denoising and deblurring techniques. Experiments show that these methods perform favorably against various existing cutting edge algorithms.