MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion
Ting Jiang Chuan Wang Xinpeng Li Ru Li Haoqiang Fan Shuaicheng Liu
In this paper, we introduce a new approach for high-quality multi-exposure
image fusion (MEF). We show that the fusion weights of an exposure can be
encoded into a 1D lookup table (LUT), which takes pixel intensity value as
input and produces fusion weight as output. We learn one 1D LUT for each
exposure, then all the pixels from different exposures can query 1D LUT of that
exposure independently for high-quality and efficient fusion. Specifically, to
learn these 1D LUTs, we involve attention mechanism in various dimensions
including frame, channel and spatial ones into the MEF task so as to bring us
significant quality improvement over the state-of-the-art (SOTA). In addition,
we collect a new MEF dataset consisting of 960 samples, 155 of which are
manually tuned by professionals as ground-truth for evaluation. Our network is
trained by this dataset in an unsupervised manner. Extensive experiments are
conducted to demonstrate the effectiveness of all the newly proposed
components, and results show that our approach outperforms the SOTA in our and
another representative dataset SICE, both qualitatively and quantitatively.
Moreover, our 1D LUT approach takes less than 4ms to run a 4K image on a PC
GPU. Given its high quality, efficiency and robustness, our method has been
shipped into millions of Android mobiles across multiple brands world-wide.
Code is available at: https://github.com/Hedlen/MEFLUT.