Moire patterns, unwanted color artifacts in images and videos, arise from the interference between spatially high-frequency scene contents and the spatial discrete sampling of digital cameras. Existing demoireing methods primarily rely on single-camera image/video processing, which faces two critical challenges: 1) distinguishing moire patterns from visually similar real textures, and 2) preserving tonal consistency and temporal coherence while removing moire artifacts. To address these issues, we propose a dual-camera framework that captures synchronized videos of the same scene: one in focus (retaining high-quality textures but may exhibit moire patterns) and one defocused (with significantly reduced moire patterns but blurred textures). We use the defocused video to help distinguish moire patterns from real texture, so as to guide the demoireing of the focused video. We propose a frame-wise demoireing pipeline, which begins with an optical flow based alignment step to address any discrepancies in displacement and occlusion between the focused and defocused frames. Then, we leverage the aligned defocused frame to guide the demoireing of the focused frame using a multi-scale CNN and a multi-dimensional training loss. To maintain tonal and temporal consistency, our final step involves a joint bilateral filter to leverage the demoireing result from the CNN as the guide to filter the input focused frame to obtain the final output. Experimental results demonstrate that our proposed framework largely outperforms state-of-the-art image and video demoireing methods.

Close set is a hypothesis utilized by the majority of machine-learning-based (ML-based) recognition algorithms, assuming all testing classes are known at training time. In real world, the more practical model is Open Set Recognition (OSR), which allows the presence of unknown classes at testing time, but requires the rejection ability of the model. The compact abating probability (CAP) model, which assumes the probability of class membership decreases in value (abates) as points move from known data toward open space, is first raised in traditional ML-based OSR method and soon become the basis of majority of later developed works. Most of convolutional-neural-network-based (CNN-based) OSR methods also adopted this model as their basis. During our exploration, however, we find that the application of CAP model to the CNN-based OSR method is restricted by the difference of its feature space from that of ML-based method. To the best of our knowledge, we are the first group who find this gap. To fill this gap, we propose a method called OpenSoftMax to transform the CNN-based methods' features by the process of SoftMax. In order to investigate performance, we further implement quantitative comparison between our OpenSoftMax method and the well-known CNN-based method OpenMax on caltech256 datasets. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposals.