Generalized Synthesis and Analysis Prior Algorithms with Application to Impulse Denoising
Hemant Kumar Aggarwal, Angshul Majumdar · 2014
This work proposes generalized synthesis and analysis prior algorithms using the split-Bregman technique for applications in impulse noise reduction. Impulse denoising is formulated as minimizing a Lp-regularized Lq-norm data mismatch term. The Lq-norm mismatch arises owing to the fact that the noise is sparse. The Lp-norm exploits the prior information that the image is sparse in a transform domain. The proposed methods have been used to reduce salt and pepper noise as well as random valued impulse noise. Peak signal to noise ratio and structural similarity index have been used to quantitatively evaluate the recovery results. A comparative study with existing IRN algorithm suggests the superiority of purposed algorithm. Our method also yields better results than the popular median filtering techniques used for denoising impulse noise.