HIGHLIGHTS
- who: Canlin Li and colleagues from the School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China, University, China have published the article: Efficient adaptive feature aggregation network for low-light image enhancement, in the Journal: PLOS ONE of 19/07/2022
- what: The authors propose an efficient adaptive feature aggregation network (EAANet) for low-light image enhancement. The authors conducted an extensive comparison with some state-of-the-art methods in terms of PSNR SSIM parameters computations and running time on LOL and MIT5K datasets and the experiments show that the . . .
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