The 11th Iranian and the first International Conference on Machine Vision and Image Processing

Removing Mixture of Gaussian and Impulse Noise of Images Using Sparse Coding

Mahsa Malekzadeh, Saeed Meshgini, Reza Afrouzian, Ali Farzamnia3 , Sobhan Sheykhivand
The 11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020)

Abstract

Real images contain different types of noises and a very difficult process is to remove mixed noise in any type of them. Additive White Gaussian Noise (AWGN) coupled with Impulse Noise (IN) is a typical method. Many mixed noise removal methods are based on a detection method that generates artificial products in case of high noise levels. In this article, we suggest an active weighted approach for mixed noise reduction, defined as Weighted Encoding Sparse Noise Reduction (WESNR), encoded in sparse non-local regulation. The algorithm utilizes a non-local self-similarity feature of image in the sparse coding framework and a pre-learned Principal Component Analysis (PCA) dictionary. Experimental results show that both the quantitative and the visual quality, the proposed WESNR method achieves better results of the other technique in terms of PSNR.

Keywords: Mixed Noise Removal, Weighted Encoding, Nonlocal Regularization, Sparse Coding



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