Abstract: To remove salt-and-pepper noise with minimal degradation(e.g.,edge blurring,color deviation,and stripe)of image intrinsic properties,we present an adaptive salt-and-pepper denoising method based on a deep residual network.The main idea of this paper is to simplify image denoising into two steps.Firstly,in order to enable the network model to handle different-densities salt-and-pepper noises and improve the robustness of the network model,we remove the high frequency information using adaptive windows as the first step.Secondly,we construct an effective deep residual network model to train a function which can map the pre-processed images to their corresponding ground truths.Qualitative and quantitative experiments show that not only can our method avoid problems such as color distortions and streaks,but also our method outperforms the state-of-the-art learning-based and traditional approaches,in terms of both handling inputs with different levels of noises and revealing high-fidelity image edges.Meanwhile,the performance on BSD300 evaluated in PSNR shows superiority over the competitors.