整理:AI算法与图像处理
CVPR2022论文和代码整理:https://github.com/DWCTOD/CVPR2022-Papers-with-Code-Demo
ECCV2022论文和代码整理:https://github.com/DWCTOD/ECCV2022-Papers-with-Code-Demo
标题:MuLUT: Cooperating Mulitple Look-Up Tables for Efficient Image Super-Resolution
论文:
https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136780234.pdf
主页:https://mulut.pages.dev/
代码:https://github.com/ddlee-cn/MuLUT
边缘设备的高分辨率屏幕刺激了对高效图像超分辨率(SR)的强烈需求。一项新兴的研究,SR-LUT,通过将查找表(LUT)与基于学习的SR方法相结合来响应这一需求。然而,单个LUT的大小随着其索引容量的增加呈指数级增长。因此,单个LUT的感受野受到限制,导致性能低下。为了解决这个问题,我们通过支持多个LUT(称为MuLUT)的合作来扩展SR-LUT。首先,我们设计了两种新的互补索引模式,并行构造了多个LUT。其次,我们提出了一种重新索引机制,以实现多个LUT之间的分层索引。在这两种方式中,MuLUT的总大小与其索引容量呈线性关系,从而产生了一种获得优异性能的实用方法。我们检查了MuLUT在五个SR基准上的优势。MuLUT比SR-LUT实现了显著的改进,最高可达1.1dB PSNR,同时保持了其效率。此外,我们扩展了MuLUT以解决拜耳图案图像的去马赛克问题,在两个基准上大大超过了SR-LUT。
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