A Very Fast and Efficient Multistage Selective Convolution Filter for Removal of Salt and Pepper Noise
This repository contains the MATLAB codes for the implementation of the paper "A very fast and efficient multistage selective convolution filter for removal of salt and pepper noise."
Rafiee, A.A., Farhang, M. A very fast and efficient multistage selective convolution filter for removal of salt and pepper noise. J Ambient Intell Human Comput (2022). https://doi.org/10.1007/s12652-022-03747-7
https://doi.org/10.1007/s12652-022-03747-7
In this paper we propose a multistage selective convolution filter (MSCF) for fast and efficient removal of salt-and-pepper noise (SPN) in digital images. By avoiding the use of order statistics or other computationally expensive procedures, the proposed denoising algorithm is efficiently implemented using convolution blocks, thereby a significant reduction in computation time is achieved. Moreover, in each stage of the proposed structure, a weighted mean filter of an appropriate kernel size is employed to selectively restore a set of noisy pixels qualified by a reliability criterion to improve the performance. The simulation results show that the proposed method denoises much faster than all its competent counterparts, while it achieves a significant performance in both quantitative criteria and visual effects. While noise removal by traditional methods such as AMF takes about 1.092 s and by fast state-of-the-art methods such as NAHAT takes about 0.065 s on each image of the BSDS500 dataset on average, the proposed method dramatically reduces the execution time to 0.005 s.
