Internally Referenced Low-Light Enhancement

A new arXiv paper presents an internally referenced framework for self-supervised low-light image enhancement.

The method derives pseudo ground truth, structural constraints, and adaptive gain priors directly from the degraded input image.

These cues guide illumination correction and spatially aware denoising, helping preserve texture while suppressing noise.

The authors report state-of-the-art performance, reducing reliance on paired training data.