RTF-Net, combining residual blocks with a convolutional vision transformer, reports the highest PSNR for salt-and-pepper denoising on most tested images.
A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise
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abstract
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.
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Residual Transformer Fusion Network for Salt and Pepper Image Denoising
RTF-Net, combining residual blocks with a convolutional vision transformer, reports the highest PSNR for salt-and-pepper denoising on most tested images.