Analysis of wavelet image denoising model in Besov spaces
Qibin Fan, Minkai Jiang, Wenping Xiao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
In this paper, we discuss the image denoising model which DeVore et al. had established, in which both distance and smoothness can be measured by the objective function, and analysis the model for wavelet image denoising in the Besov spaces with p = q. In addition, we give the exact thresholds for the model, and prove that for 0 < p <1 the effect of noise removal using our methods is in between hard wavelet shrinkage and soft wavelet shrinkage. For the case 0 < p < 1 and 1 ≤ p ≤ ∞, which refers to the problems on the convergence of the iteration of the equations and on the complexity of computation, we give the simplified algorithms. Comparing the threshold given by this paper with Lorenz threshold, we conclude that the former is more meticulous than the latter for the model.