StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement

被引:23
|
作者
Song, Yuda [1 ]
Qian, Hui [1 ]
Du, Xin [2 ]
机构
[1] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China
[2] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China
来源
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) | 2021年
关键词
NETWORK;
D O I
10.1109/ICCV48922.2021.00409
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image enhancement is a subjective process whose targets vary with user preferences. In this paper, we propose a deep learning-based image enhancement method covering multiple tonal styles using only a single model dubbed StarEnhancer. It can transform an image from one tonal style to another, even if that style is unseen. With a simple one-time setting, users can customize the model to make the enhanced images more in line with their aesthetics. To make the method more practical, we propose a well-designed enhancer that can process a 4K-resolution image over 200 FPS but surpasses the contemporaneous single style image enhancement methods in terms of PSNR, SSIM, and LPIPS. Finally, our proposed enhancement method has good interactability, which allows the user to fine-tune the enhanced image using intuitive options.
引用
收藏
页码:4106 / 4115
页数:10
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