Awesome
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. 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
updating...