Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution

Guandu Liu, Yukang Ding, Mading Li, Ming Sun, Xing Wen, Bin Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 12217-12226

Look-up table (LUT)-based methods have shown the great efficacy in single image super-resolution (SR) task. However, previous methods don't delve into the essential reason of restricted receptive field (RF) size in LUT, which is caused by the interaction of space and channel features in vanilla convolution. To enlarge RF with contained LUT sizes, we propose a novel Reconstructed Convolution(RC) module, which decouples channel-wise and spatial calculation. It can be formulated as n^2 1D LUTs to maintain nxn receptive field, which is obviously smaller than nxn D LUT formulated before. The LUT generated by our RC module reaches less than 1/10000 storage compared with SR-LUT baseline. The proposed Reconstructed Convolution module based LUT method, termed as RCLUT, can enlarge the RF size by 9 times than the state-of-the-art LUT-based SR method and achieve superior performance on five popular benchmark dataset. Moreover, the efficient and robust RC module can be used as a plugin to improve other LUT-based SR methods. The code is available at https://github.com/RC-LUT/RC-LUT.git.