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Low-Light Image Enhancement Using a Simple Network Structure
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DOI：10.1109/ACCESS.2023.3290490.png)
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摘要
En 中文
Under low-light conditions, captured images can be affected by unsatisfactory lighting conditions. Low-light image enhancement called LLIE is a digital image processing to obtain natural normal-light images from the low-light image. LLIE includes three main tasks: reducing noise and artifacts, preserving edges and textures, and reproducing natural brightness and color. In recent years, many types of research have focused on deep-learning-based approaches that can achieve excellent performance. However, one primary problem with these methods is that inference time is long owing to complex network structures. To solve the trade-off between the performance and implementation time, we propose a simple network with effective modules. We utilize a U-Net structure and pre-processing is added to preserve edges and textures. Moreover, we embed Channel Attention to restore color and illumination, Res FFT-ReLU to reduce noise, and Pixel Shuffler to preserve the high-frequency components. According to our experimental results, the proposed method achieves better performance and faster inference time than conventional LLIE methods.
Keyword:
INDEX TERMS Low-light image enhancement
deep learning
channel attention
image restoration
residual learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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