BlindPSNR: A No-Reference Fidelity Predictor for Low-Light Image Enhancement
Low-light image enhancement (LLIE) methods involve tunable parameters that are typically fixed, often leading to performance degradation when applied across scenes. Manually selecting the best configuration, however, can be time-consuming and not always practical. Peak signal-to-noise ratio (PSNR) is the natural fidelity criterion for automating parameter selection, yet it requires a ground-truth reference that is typically unavailable. To our knowledge, no learning-based method addresses no-reference PSNR prediction for low-light image enhancement; the natural surrogate, no-reference image quality assessment (NR-IQA), targets perceptual quality rather than signal fidelity, and all seven ...
| Authors | Mingzhe Lyu, Jinqiang Cui, Hong Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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