A Fast Iterative Method for Removing Sparse Noise from Sparse Signals Reconstructing a signal corrupted by impulsive noise is of high importance in several applications, including impulsive noise removal from images, audios and videos, and separating texts from images. Investigating this problem, in this paper we propose a new method to reconstruct a noisecorrupted signal where both signal and noise are sparse but in different domains. We apply our algorithm for impulsive noise (Salt andPepper Noise (SPN) and RandomValued Impulsive Noise (RVIN)) removal from images and compare our results with other notable algorithms in the literature. Simulation indicates show that our algorithm is not only simple and fast, but also it outperforms the other stateoftheart methods in terms of reconstruction quality andor complexity.

Additive Noise Level Estimation Based on Singular Value Decomposition (SVD) in Natural Digital Images True Noise level estimation is a seminal research of interest in the topic of digital image processing especially in blind noise removal methods. In this study, an estimation of additive white Gaussian noise (AWGN) in digital natural images is introduced. The adaptive noise level estimation is designed mainly based on singular value decomposition (SVD) of the natural images. The proposed technique contains two pivotal stages. Firstly, typical noise level estimate is utilized in order to manipulate the algorithm factors to be used in the second stage of the proposed technique. Secondly, the adjusted parameters are used in SVD in order to speed up the estimation processes and increase the accuracy rate of the noise level estimation. The experimental results depict that the proposed algorithm performs professionally over a several ranges of visual details which is presented in PSNR and MSE in AWGN removal methods. In addition, in terms of time complexity, the proposed algorithm in second stage shows significant performance in terms of computational load and achieves high running speed.