Reconstructed Convolution Module Based Look-Up Tables for Efficient
  Image Super-Resolution
Guandu Liu Yukang Ding Mading Li Ming Sun Xing Wen Bin Wang
Look-up table(LUT)-based methods have shown the great efficacy in single
image super-resolution (SR) task. However, previous methods ignore 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.
They can only increase the RF at the cost of linearly increasing LUT size. 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 $n\times n$ receptive field,
which is obviously smaller than $n\times n$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/liuguandu/RC-LUT.