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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

image

Overview

The core idea of our paper is RC Module. image

Usage

Our code follows the architecture of 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. And you can also prepare SRBenchmark

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

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