Single Image De-noising via Staged Memory Network

Single image de-noising is an important yet under-explored task to estimate the underlying clean image from its noisy observation. It poses great challenges over the balance between over-de-noising (e.g., mistakenly remove texture details in noise-free regions) and under-de-noising (e.g., leave noisy points). Existing works solely treat the removal of noise from images as a process of pixel-wise regression and lack of preserving image details. In this paper, we firstly propose a Staged Memory Network (SMNet) consisting of noise memory stage and image memory stage for explicitly exploring the staged memories of our network in single image de-noising with different noise levels. Specifically, the noise memory stage is to reveal noise characteristics by using local-global spatial dependencies via an encoder-decoder sub-network composed of dense blocks and noise-aware blocks. Taking the residual result between the input noise image and the prediction of the noise memory stage as input, the image memory stage continues to get a noise-free and well-reconstructed output image via a contextual fusion sub-network with contextual blocks and a fusion block. Solid and comprehensive experiments on three tasks (i.e. synthetic and real data, and blind de-noising) demonstrate that our SMNet can significantly achieve better performance compared with state-of-the-art methods by cleaning noisy images with various densities, scales and intensities while keeping the image details of noise-free regions well-preserved. Moreover, interpretability analysis is added to further prove the ability of our composed memory stages.