Research map / Image generation / Image restoration
Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add reflectance consistency loss LC using paired low-light instances (ablation) | PairLIE · LOL | PSNR: 17.70 -> 19.51 | Helped |
| Add reflectance consistency loss LC using paired low-light instances (ablation) | PairLIE · LOL | SSIM: 0.567 -> 0.736 | Helped |
| Add projection loss LP and P-Net (ablation) | PairLIE · LOL | PSNR: 19.50 -> 19.51 | Helped |
| Add projection loss LP and P-Net (ablation) | PairLIE · LOL | SSIM: 0.724 -> 0.736 | Helped |
| Add illumination initialization and smoothness prior terms (ablation) | PairLIE · LOL | PSNR: 14.69 -> 19.51 | Helped |
| Add illumination initialization and smoothness prior terms (ablation) | PairLIE · LOL | SSIM: 0.684 -> 0.736 | Helped |
Closest papers by meaning
- IniRetinex: Rethinking Retinex-type Low-Light Image Enhancer via Initialization Perspective (AAAI 2025 · similarity 0.88)
- Zero-Reference Low-Light Enhancement via Physical Quadruple Priors (CVPR 2024 · similarity 0.87)
- Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios (ICLR 2025 · similarity 0.87)
- Zero-Shot Low-Light Image Enhancement via Latent Diffusion Models (AAAI 2025 · similarity 0.87)
- MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space (NeurIPS 2024 · similarity 0.86)
- Enhancing Low-Light Images: A Synthetic Data Perspective on Practical and Generalizable Solutions (AAAI 2025 · similarity 0.86)
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