Name
Abstract | ||
|---|---|---|
ABSTRACTStructure and texture information is critically important for low-light image enhancement, in terms of stable global adjustment and fine details recovery. However, most existing methods tend to learn the structure and texture of low-light images in a coupled manner, without well considering the heterogeneity between them, which challenges the capability of the model to learn both adequately. In this paper, we tackle this problem in a divide and conquer strategy, based on the observation that the structure and texture representations are highly separated in the frequency spectrum. Specifically, we propose a Structure and Texture Aware Network (STAN) for low-light image enhancement, which consists of a structure sub-network and a texture sub-network. The former exploits the low-pass characteristic of the transformer to capture low-frequency-related structural representation. While the latter builds upon central difference convolution to capture high-frequency-related texture representation. We establish the Multi-Spectrum Interaction (MSI) module between two sub-networks to bidirectionally provide complementary information. In addition, to further elevate the capability of the model, we introduce a dual distillation scheme that assists the learning process of two sub-networks via counterparts' normal-light structure and texture representations. Comprehensive experiments show that the proposed STAN outperforms the state-of-the-art methods qualitatively and quantitatively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3503161.3548359 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinghao Zhang | 1 | 0 | 0.68 |
| Jie Huang | 2 | 0 | 0.34 |
| Mingde Yao | 3 | 0 | 1.01 |
| Man Zhou | 4 | 0 | 0.34 |
| Feng Zhao | 5 | 0 | 4.06 |