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Moire artifacts are common in digital photography, resulting from theinterference between high-frequency scene content and the color filter array ofthe camera. Existing deep learning-based demoireing methods trained on largescale datasets are limited in handling various complex moire patterns, andmainly focus on demoireing of photos taken of digital displays. Moreover,obtaining moire-free ground-truth in natural scenes is difficult but needed fortraining. In this paper, we propose a self-adaptive learning method fordemoireing a high-frequency image, with the help of an additional defocusedmoire-free blur image. Given an image degraded with moire artifacts and amoire-free blur image, our network predicts a moire-free clean image and a blurkernel with a self-adaptive strategy that does not require an explicit trainingstage, instead performing test-time adaptation. Our model has two sub-networksand works iteratively. During each iteration, one sub-network takes the moireimage as input, removing moire patterns and restoring image details, and theother sub-network estimates the blur kernel from the blur image. The twosub-networks are jointly optimized. Extensive experiments demonstrate that ourmethod outperforms state-of-the-art methods and can produce high-qualitydemoired results. It can generalize well to the task of removing moireartifacts caused by display screens. In addition, we build a new moire dataset,including images with screen and texture moire artifacts. As far as we know,this is the first dataset with real texture moire patterns.

Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs