A novel denoising algorithm for medical images based on the non‐convex non‐local similar adaptive regularization

Abstract

Sparse representation is a powerful statistical image modelling technique and has been successfully applied to image denoising. For a given patch, a non-convex non-local similarity adaptive method is adopted for sparse representation of images. First, it uses the autoregressive model to perform dictionary learning from sample patch datasets. Second, the sparse representation of an image introduces non-convex non-local self-similarity as the regularization term. In order to make better use of the sparse regularization method for image denoising, the parameters used in this study are estimated using adaptive methods. This model is more efficient and accurate, Compared with K-means singular value decomposition (KSVD) algorithm, a generalized K-means clustering method, total variation of population sparsity (GSTV) algorithm, adaptive sparse domain selection (ASDS) algorithm, forward denoising convolutional neural network (DnCNNs), a fast and flexible Convolutional Neural Network image denoising method (FFNNet) and operator-splitting algorithm to minimize the Euler elastica functional (OSEEF). Image noise-reduction experiments confirmed that using the adaptive regularization method, the results in peak signal to noise ratio (PSNR) and visual opinion are better than other algorithms.