Image Denoising Algorithm Based on Adaptive Singular Value Threshold

Non-local similarity images play a huge role in image denoising tasks. Many of the existing denoising algorithms have problems in that the edge information is too smooth, the reconstruction details are insufficient, and artifacts are easily generated while removing noise. In order to solve these shortcomings and improve the denoising accuracy, we propose a denoising algorithm based on non-local similarity and adaptive singular value threshold (ASVT). The algorithm consists of three basic steps: block matching grouping, ASVT denoising, and aggregation. First, similar image patches are grouped by block matching method, and each similar block group is used as a group matrix for each column of the matrix. Then, under the framework of image non-local similarity and low rank approximation, the denoising problem is transformed into low rank matrix approximation problem, which is solved by ASVT. Finally, all processed image patches are aggregated to produce an initial denoised image. In order to effectively avoid the influence of noise residual on denoising, the denoising result is further improved by the back projection strategy, and more detailed features are retained. Experimental results clearly show that the proposed algorithm is competitive with the current state-of-the-art denoising algorithms in terms of both quantitative measure and subjective visual quality and can retain more details and improve the smoothing problem.