An Improved Method of Wavelets Basis Image Denoising Using Besov Norm Regularization
Hong Yang, Yiding Wang · 2007
This paper proposes art improved image denoising algorithm which bases on wavelets thresholding - and uses the Besov norm regularization. Given a noisy image u0and suppose the target image u belongs to we need to solve the Besov space Baq(Lp) optimization problem: min ||u||qBaq(Lp)+lambda/2|| u - u0||2L2The existing algorithms used the fixed parameters p, q, a of Baq(Lp) to determine the threshold of wavelets reconstruction. Since different parts of an image may have different smoothness properties, and wavelet coefficients denote different frequency subbands of an image, the subimages at each wavelets scale level may have distinct smoothness properties. The larger the a is, the smoother the images are in Baq(Lp). Taking the smoothness index a into account, we try to optimize the alphajat different wavelet scale j with p,q fixed. Experimental results show that our method achieves better denoising effect with higher PSNR than the alpha fixed method.