Image Denoising Using Hybrid Singular Value Thresholding Operators

Abstract

Truncated singular value decomposition (TSVD) is a simple and efficient technique for patch-based image denoising, in which a hard thresholding operator is utilized to set some small singular values to zero. Before performing the hard thresholding, the noise variance should be accurately estimated in order to determine the rank of the patch matrices. However, when the noise level is high, the denoisied results from the TSVD still contain some residual noise. To solve this problem, we present a hybrid thresheldoing strategy that combines a hard thresholding operator and a soft one. The former is directly reused the thresholding derived from TSVD, the latter is derived by minimum variance estimator. Simulation experiments are conducted to verify the effectiveness of the proposed method. Experimental results show that the method can effectively denoise the images with high level noise.

Keywords

• Image denoising
• low-rank matrix estimation
• minimum variance estimate
• nonlocal self-similarity
• singular value thresholding