Abstract: In order to improve the accuracy of centroid location for noise-containing spot images, an image denoising method based on low-rank and sparse decomposition (LRSD) is proposed. Relative total variation (RTV) is 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 capability of LRSD method. Alternating direction multiplier method (ADMM) is used to solve this convex problem iteratively. Moreover, to enhance the detail-preserving capability of LRSD denoising method, the immune disturbance and annealing strategy is introduced respectively to improve particle swarm optimization (PSO) algorithm, and is applied to LRSD singular value threshold adaptive selection. Furthermore, it can form an image denoising fusion algorithm, which has 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 such as non-local mean filtering (NLMF) and BM3D, and it has also remarkably improved the accuracy of centroid location for noise-containing spot images.

Key words: spot image, centroid location, image denoising, low-rank sparse decomposition, particle swarm optimization