Detail-revealing Deep Video Super-resolution
원문 초록
Previous CNN-based video super-resolution approaches need to align multiple frames to the reference. In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results. We accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN framework. Analysis and experiments show the suitability of this layer in video SR. The final end-to-end, scalable CNN framework effectively incorporates the SPMC layer and fuses multiple frames to reveal image details. Our implementation can generate visually and quantitatively high-quality results, superior to current state-of-the-arts, without the need of parameter tuning.
함께 살펴볼 논문
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Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution
DualX-VSR: Dual Axial Spatial$\times$Temporal Transformer for Real-World Video Super-Resolution without Motion Compensation
Super-Resolution Generative Adversarial Networks based Video Enhancement
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Real-World Super-Resolution via Kernel Estimation and Noise Injection
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
토론
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