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

2022/06/11 by Renwei Yang, Yang, Renwei, Yike Liu +3
Computer Science · #Image and Signal Denoising Methods #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2206.05520

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.

Related

• A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise
• Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
• A salt and pepper noise image denoising method based on the generative classification
• Adaptive Noise Reduction Scheme for Salt and Pepper
• Salt-and-Pepper Noise Removal Based on Sparse Signal Processing