  In recent years, self-supervised denoising methods have shown impressive
performance, which circumvent painstaking collection procedure of noisy-clean
image pairs in supervised denoising methods and boost denoising applicability
in real world. One of well-known self-supervised denoising strategies is the
blind-spot training scheme. However, a few works attempt to improve blind-spot
based self-denoiser in the aspect of network architecture. In this paper, we
take an intuitive view of blind-spot strategy and consider its process of using
neighbor pixels to predict manipulated pixels as an inpainting process.
Therefore, we propose a novel Mask Guided Residual Convolution (MGRConv) into
common convolutional neural networks, e.g. U-Net, to promote blind-spot based
denoising. Our MGRConv can be regarded as soft partial convolution and find a
trade-off among partial convolution, learnable attention maps, and gated
convolution. It enables dynamic mask learning with appropriate mask constrain.
Different from partial convolution and gated convolution, it provides moderate
freedom for network learning. It also avoids leveraging external learnable
parameters for mask activation, unlike learnable attention maps. The
experiments show that our proposed plug-and-play MGRConv can assist blind-spot
based denoising network to reach promising results on both existing
single-image based and dataset-based methods.
