A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise
This is an incremental improvement for image denoising in computer vision, addressing a specific noise type.
The paper tackles the challenge of denoising salt and pepper noise in images, especially at high noise levels, by proposing a two-step algorithm called NLSF-CNN that combines non-local switching filter pre-processing with CNN training, and it outperforms state-of-the-art methods with few training images.
The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.