Abstract
Denoising images is a classical problem in low-level computer vision. In this paper, we propose an algorithm which can remove iteratively salt and pepper noise based on neighbourhood while preserving details. First, we compute the probability of different window without free noise pixel by noise ratio, and then determine the size of window. After that the corrupted pixel is replaced by the weighted eight neighbourhood pixels. If the neighbourhood information does not satisfy the de-noising condition, the corrupted pixels will recover in the subsequent iterations.
Highlights
Salt and pepper noise (SPN) usually comes from the image sensor, transmission channel and decoding processing
We proposed a new algorithm for SPN called the iterative restoration based on neighbourhood information
Five different methods include median filtering (MF), noise adaptive fuzzy switching median filter (NAFSMF) [3], long-range correlation filter (LRC) [9], adaptive median filter (AMF) [2], adaptive weighted mean filter (AWMF) [4] are used to evaluate the performance of our proposed algorithm
Summary
Salt and pepper noise (SPN) usually comes from the image sensor, transmission channel and decoding processing. . if choosing a big filtering window ( ) or much larger one, that means amount of calculation in the low noise image recovering. We proposed a new algorithm for SPN called the iterative restoration based on neighbourhood information. When eight-neighbourhood information of the destroyed pixel are not complete or even are not available, the recovery needs through good blocks from global of image to assist the process. It will be an iteratively process until the eight-neighbourhood can remove noise pixels. The detection of noise level is first analyzed, and the removing noise algorithm is proposed based on the neighbourhood pixels.
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