A Low-Rank and Sparse Decomposition-Based Method of Improving the Accuracy of Sub-Pixel Grayscale Centroid Extraction for Spot Images

In order to improve the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images, an image denoising method based on low-rank and sparse decomposition (LRSD) was proposed in this paper. Relative total variation (RTV) was introduced into the method on the basis of the weighted nuclear norm minimization (WNNM) model to construct a new RTV-WNNM model, so as to enhance the denoising accuracy and detail-preserving capability of LRSD method. Alternating direction multiplier method (ADMM) was used to solve this convex problem iteratively. Moreover, in view that the existing LRSD denoising method had insufficient detail-preserving capability, an adaptive selection method of LRSD singular value threshold based on the improved elitist clone selection algorithm (ECSA) was also raised in this paper. The constructed RTV-WNNM model taken as the objective function, the improved ECSA could realize equilibrium between search in depth and optimization in breadth through the adaptive chaotic mutation operator and antibody reselection strategy based on antibody concentration and antigen affinity moment. Furthermore, it could reach rapid self-adaption to acquire a low-rank threshold and then form an image denoising fusion algorithm, which had both denoising and detail-preserving capabilities. Simulation and experimental results show that the proposed method can reach better results in both quantity measure and visual quality than the state-of-the-art image denoising methods, and it has also remarkably improved the accuracy of sub-pixel grayscale centroid extraction for noise-containing spot images.