SALVE: Self-supervised Adaptive Low-light Video Enhancement

TL;DR

SALVE is a self-supervised, adaptive, and computationally efficient method for enhancing low-light videos by combining retinex-based enhancement with ridge regression, outperforming prior methods in user preference.

Contribution

Introduces SALVE, a novel low-light video enhancement approach that combines traditional retinex techniques with learning-based ridge regression in a self-supervised framework.

Findings

87% of users preferred SALVE over previous methods

SALVE effectively enhances key frames and propagates improvements to entire videos

The method is robust, adaptive, and computationally inexpensive

Abstract

A self-supervised adaptive low-light video enhancement method, called SALVE, is proposed in this work. SALVE first enhances a few key frames of an input low-light video using a retinex-based low-light image enhancement technique. For each keyframe, it learns a mapping from low-light image patches to enhanced ones via ridge regression. These mappings are then used to enhance the remaining frames in the low-light video. The combination of traditional retinex-based image enhancement and learning-based ridge regression leads to a robust, adaptive and computationally inexpensive solution to enhance low-light videos. Our extensive experiments along with a user study show that 87% of participants prefer SALVE over prior work.