A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal

TL;DR

This paper proposes a two-stage neural network approach for removing non-extreme salt-and-pepper noise, addressing real-world variations in noise pixel values beyond traditional methods.

Contribution

It introduces a novel two-stage CNN framework that detects and restores salt-and-pepper noise with non-standard pixel values, improving robustness in practical scenarios.

Findings

Effective detection of non-extreme noise pixels

Improved denoising performance over traditional methods

Robustness to real-world noise variations

Abstract

There are several previous methods based on neural network can have great performance in denoising salt and pepper noise. However, those methods are based on a hypothesis that the value of salt and pepper noise is exactly 0 and 255. It is not true in the real world. The result of those methods deviate sharply when the value is different from 0 and 255. To overcome this weakness, our method aims at designing a convolutional neural network to detect the noise pixels in a wider range of value and then a filter is used to modify pixel value to 0, which is beneficial for further filtering. Additionally, another convolutional neural network is used to conduct the denoising and restoration work.