[ECCV 2022]MuLUT: Cooperating Multiple Look-Up Tables for Efficient Image Super-Resolution
Channel: Jiacheng Li
Date: 25/10/2022

Quick Intro:

A neural network with restricted receptive field (RF) can be converted into a LUT, yielding a learnable and efficient solution for image super-resolution to avoid substantial computations on edge devices.

We propose cooperating multiple LUTs (MuLUT) to overcome the intrinsic limitation of single LUT (i.e., the restricted RF).

Our method significantly outperforms the single-LUT solution, while preserving its efficiency.

Authors:
Jiacheng Li*, Chang Chen*, Zhen Cheng, and Zhiwei Xiong

Project page: https://mulut.pages.dev/
Code and models: https://github.com/ddlee-cn/MuLUT
Paper: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136780234.pdf