论文标题
探索图像超分辨率的稀疏性以提高推理
Exploring Sparsity in Image Super-Resolution for Efficient Inference
论文作者
论文摘要
当前基于CNN的超分辨率（SR）方法在空间中均匀分配的计算资源平均处理所有位置。但是，由于低分辨率（LR）图像中的缺失细节主要存在于边缘和纹理区域中，因此这些平坦区域所需的计算资源较少。因此，现有的基于CNN的方法涉及扁平区域中的冗余计算，这增加了其计算成本并限制了其在移动设备上的应用。在本文中，我们探讨了图像SR中的稀疏性，以提高SR网络的推理效率。具体而言，我们开发了一个稀疏的掩码SR（SMSR）网络，以学习稀疏面具以修剪冗余计算。在我们的SMSR中，空间口罩学会识别“重要”区域，而频道面具学会在那些“不重要”区域中标记冗余频道。因此，冗余计算可以准确地定位和跳过，同时保持可比性的性能。已经证明，我们的SMSR实现了最先进的性能，X2/3/4 SR减少了41％/33％/27％的拖鞋。代码可在以下网址提供：https：//github.com/longguangwang/smsr。
Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-resolution (LR) images mainly exist in regions of edges and textures, less computational resources are required for those flat regions. Therefore, existing CNN-based methods involve redundant computation in flat regions, which increases their computational cost and limits their applications on mobile devices. In this paper, we explore the sparsity in image SR to improve inference efficiency of SR networks. Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation. Within our SMSR, spatial masks learn to identify "important" regions while channel masks learn to mark redundant channels in those "unimportant" regions. Consequently, redundant computation can be accurately localized and skipped while maintaining comparable performance. It is demonstrated that our SMSR achieves state-of-the-art performance with 41%/33%/27% FLOPs being reduced for x2/3/4 SR. Code is available at: https://github.com/LongguangWang/SMSR.
