Progressive Moire Removal and Texture Complementation for Image Demoireing
Yuzhen Niu, Zhihua Lin, Wenxi Liu, Wenzhong Guo
Abstract
Taking photos of digital screens often produces color-distorting moire patterns caused by inconsistency between the color filter array of cameras and the sub-pixel layout of screens, which severely degrades the quality of photos. Most existing demoireing methods employ multi-stream network architecture to simultaneously process the same moire image with different resolutions, but they neglect the complementarity among different resolutions. In this paper, we propose a novel moire removal model to address this issue. Unlike the existing multi-stream based approaches, in this model, we present a progressive texture complementation block to exploit the complementary information from different resolutions in order to progressively remove moire textures and restore image content. Additionally, we propose a residual moire removal block, in which the depthwise separable convolution is utilized to remove moire from image while reducing computation overhead. This block also includes a local color correction structure, which is used to correct color shifts presented in the moire images. Experimental results on two public datasets show that our method outperforms state-of-the-art methods. Besides, the quantity of parameters and FLOPs of our model are tens of times fewer than the off-the-shelf models. Furthermore, our network framework can adapt well to another low-level vision task, rain removal, in which our model also achieves state-of-the-art performance.