Noise-Aware Texture-Preserving Low-Light Enhancement
This addresses the problem of enhancing low-light images while preserving textures and reducing noise for computer vision applications, though it appears incremental.
The authors tackled low-light image enhancement by proposing NATLE, a noise-aware texture-preserving method based on a Retinex model, which achieved superior performance on common datasets.
A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise removal and natural texture preservation through a low-complexity solution. Its cost function includes an estimated piece-wise smooth illumination map and a noise-free texture-preserving reflectance map. Afterwards, illumination is adjusted to form the enhanced image together with the reflectance map. Extensive experiments are conducted on common low-light image enhancement datasets to demonstrate the superior performance of NATLE.