  In recent years, there has been an unprecedented upsurge in applying deep
learning approaches, specifically convolutional neural networks (CNNs), to
solve image denoising problems, owing to their superior performance. However,
CNNs mostly rely on Gaussian noise, and there is a conspicuous lack of
exploiting CNNs for salt-and-pepper (SAP) noise reduction. In this paper, we
proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in
gray-scale and color images. To meet this objective, we introduce a new
selective convolutional (SeConv) block. SeConvNet is compared to
state-of-the-art SAP denoising methods using extensive experiments on various
common datasets. The results illustrate that the proposed SeConvNet model
effectively restores images corrupted by SAP noise and surpasses all its
counterparts at both quantitative criteria and visual effects, especially at
high and very high noise densities.
