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.

翻译：近年来，由于深度学习方法（特别是卷积神经网络）在图像去噪问题中的卓越性能，其应用呈现出前所未有的增长态势。然而，卷积神经网络主要针对高斯噪声设计，在椒盐噪声抑制方面的研究明显不足。本文提出了一种名为SeConvNet的深度卷积神经网络模型，用于抑制灰度图像和彩色图像中的椒盐噪声。为实现该目标，我们引入了一种新型选择性卷积模块。通过在多类常见数据集上开展大量实验，将SeConvNet与当前最优的椒盐噪声去噪方法进行比较。结果表明，所提出的SeConvNet模型能有效恢复被椒盐噪声污染的图像，在定量指标和视觉效果上均超越所有对比方法，尤其在极高噪声密度下表现尤为突出。