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Single low-light image brightening using learning-based intensity mapping
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DOI：10.1016/j.neucom.2022.08.042.png)
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摘要
En 中文
Inspired by image-to-curve transformation and multi-exposure fusion, in this paper, we have developed a new method to treat the low light image enhancement tasks as an extended problem with multiple vir-tual exposures by a non-linear intensity mapping function. Considering that existing image-to-curve methods have difficulty in obtaining the desired detail and brightness recovery in any one iteration with-out relying on any ground truth, we propose a virtual multi-exposure fusion strategy to merge the out-puts from these different iterations. Specifically, a simple CNN is trained to learn a pixel-wise intensity mapping function and accordingly adjust a given image multiple times. Then the results of all iterations are retained together with the original input image for fusion via a WGIF-based Multi-scale pyramid to obtain a final enhanced output. We present experimental results to demonstrate the effectiveness of the new technique and its state-of-the-art performances.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Single image brighten
CNN
Unpaired training
Multi-scale fusion
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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