WESNR : Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization

Mixed Noise Removal by Weighted Encoding withSparse Nonlocal Regularization by Jielin Jiang, Lei Zhang, and Jian Yang

Abstract—Mixed noise removal from natural images is a challenging task since the noise distribution usually does nothave a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupledwith impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of impulsenoise pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet eff ective method,namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms ofboth quantitative measures and visual quality.

The attendant implementation is on Lei Zhang's page.

Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.