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RC-LUT
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RC-LUT
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## Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution [Guandu Liu*], Yukang Ding, Mading Li, Ming Sun, Xing Wen and [Bin Wang#] ## Efficiency  ## Overview The core idea of our paper is RC Module.  ## Usage Our code follows the architecture of [MuLUT](https://github.com/ddlee-cn/MuLUT). In the sr directory, we provide the code of training RC-LUT networks, transferring RC-LUT network into LUts, finetuning LUTs, and testing LUTs, taking the task of single image super-resolution as an example. In the `common/network.py`, `RC_Module` is the core module of our paper. ### Dataset Please following the instructions of [training](./data/DIV2K/README.md). And you can also prepare [SRBenchmark](./data/DIV2K/README.md) ### Installation Clone this repo ``` git clone https://github.com/liuguandu/RC-LUT ``` Install requirements: torch>=1.5.0, opencv-python, scipy ### Train First, please train RC network follow next code ``` sh ./sr/5x57x79x9MLP_combined.sh ``` updating...
