WaveNet: Wave-Aware Image Enhancement
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
As a low-level vision task, image enhancement is widely used in various computer vision applications. Recently, multiple methods combined with CNNs, MLP, Transformer, and the Fourier transform have achieved promising results on image enhancement tasks. However, these methods cannot achieve a balance between accuracy and computational cost. In this paper, we formulate the enhancement into a signal modulation problem and propose the WaveNet architecture, which performs well in various parameters and improves the feature expression using wave-like feature representation. Specifically, to better capture wave-like feature representations, we propose to represent a pixel as a sampled value of a signal function with three wave functions (Cosine Wave (CW), Sine Wave (SW), and Gating Wave (GW)) inspired by the Fourier transform. The amplitude and phase are required to generate the wave-like features. The amplitude term includes the original contents of features, and the phase term modulates the relationship between various inputs and fixed weights. To dynamically obtain the phase and the amplitude, we build the Wave Transform Block (WTB) that adaptively generates the waves and modulates the wave superposition mode. Based on the WTB, we establish an effective architecture WaveNet for image enhancement. Extensive experiments on six real-world datasets show that our model achieves better quantitative and qualitative results than state-of-the-art methods. The source code and pretrained model are available at https://github.com/DeniJsonC/WaveNet.
Description
CCS Concepts: Computing methodologies -> Image processing
@inproceedings{10.2312:pg.20231267,
booktitle = {Pacific Graphics Short Papers and Posters},
editor = {Chaine, Raphaëlle and Deng, Zhigang and Kim, Min H.},
title = {{WaveNet: Wave-Aware Image Enhancement}},
author = {Dang, Jiachen and Li, Zehao and Zhong, Yong and Wang, Lishun},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-234-9},
DOI = {10.2312/pg.20231267}
}