low_light_enhance_method 是一个记录了近几年来优秀的低光增强算法的可以即插即用的仓库。
目前实现的算法
| 算法名称 | |
|---|---|
| 2022 | URetinex_Net: URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement |
| 2022 | SCI: Toward Fast, Flexible, and Robust Low-Light Image Enhancement |
| 2022 | SNR_LLIE_Net: SNR-aware Low-Light Image Enhancement |
| 2020 | SIM_CycleGAN: Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer(没有预训练权重) |
| 2021 | RUAS: Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement |
| 2021 | Zero-DCE++: Learning to enhance low-light image via zero-reference deep curve estimation |
| 2021 | EnlightenGAN: EnlightenGAN: Deep light enhancement without paired supervision |
| 2020 | Zero-DCE: Zero-reference deep curve estimation for low-light image enhancement |
conda create -n low_light_enhance python=3.8
conda activate low_light_enhance
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
pip install opencv-python==4.6.0
| 增强前 | 增强后 | |
|---|---|---|
| URetinex-Net |  |
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| SCI | |
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| SNR_LLIE_Net | |
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| RUAS | |
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| Zero-DCE++ | |
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| Zero-DCE | |
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| EnlightenGAN | |
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