An Improved Algorithm to Enhance Recovery Stability for Low-Rank Image

Abstract

Abstract. When recovering original image for low-rank image with noise, the effect usually has not been satisfactory through minimizing matrix nuclear norm to obtain low-rank resolution. Aiming at this problem, we introduced Frobenius norm of low-rank matrix, and combined with original low-rank matrix nuclear nor to form new regular item, and utilized an augmented lagrange multiplier method to resolve the problem after convex relaxation. Through adding 2 F A item on the base of original low-rank image recovery model, we can get better denoising result for low-rank image. The experimental results indicated that, by selecting proper parameters, the improved algorithm has superior recovery result compared to traditional LRMR model on wiping off impulse noise and gaussian noise.