DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier
Convolution for Low-light Image Enhancement
- URL: http://arxiv.org/abs/2209.07937v1
- Date: Fri, 16 Sep 2022 13:56:09 GMT
- Title: DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier
Convolution for Low-light Image Enhancement
- Authors: Yunliang Zhuang, Zhuoran Zheng, Chen Lyu
- Abstract summary: Low-light image enhancement is a classical computer vision problem aiming to recover normal-exposure images from low-light images.
convolutional neural networks commonly used in this field are good at sampling low-frequency local structural features in the spatial domain.
We propose a novel module using the Fourier coefficients, which can recover high-quality texture details under the constraint of semantics in the frequency phase.
- Score: 1.2645663389012574
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low-light image enhancement is a classical computer vision problem aiming to
recover normal-exposure images from low-light images. However, convolutional
neural networks commonly used in this field are good at sampling low-frequency
local structural features in the spatial domain, which leads to unclear texture
details of the reconstructed images. To alleviate this problem, we propose a
novel module using the Fourier coefficients, which can recover high-quality
texture details under the constraint of semantics in the frequency phase and
supplement the spatial domain. In addition, we design a simple and efficient
module for the image spatial domain using dilated convolutions with different
receptive fields to alleviate the loss of detail caused by frequent
downsampling. We integrate the above parts into an end-to-end dual branch
network and design a novel loss committee and an adaptive fusion module to
guide the network to flexibly combine spatial and frequency domain features to
generate more pleasing visual effects. Finally, we evaluate the proposed
network on public benchmarks. Extensive experimental results show that our
method outperforms many existing state-of-the-art ones, showing outstanding
performance and potential.
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