Exposure difference network for low-light image enhancement

Author:

Jiang Shengqin,Mei Yongyue,Wang Peng,Liu Qingshan

Publisher

Elsevier BV

Reference47 articles.

1. SurroundNet: towards effective low-light image enhancement;Zhou;Pattern Recognit.,2023

2. S.W. Zamir, A. Arora, S. Khan, M. Hayat, F.S. Khan, M.-H. Yang, Restormer: Efficient transformer for high-resolution image restoration, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5728–5739.

3. RELLISUR: A real low-light image super-resolution dataset;Aakerberg,2021

4. L. Ma, T. Ma, R. Liu, X. Fan, Z. Luo, Toward fast, flexible, and robust low-light image enhancement, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5637–5646.

5. You only need 90k parameters to adapt light: a light weight transformer for image enhancement and exposure correction;Cui,2022

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