Unsupervised Exposure Correction

TL;DR

This paper introduces an unsupervised method for exposure correction that eliminates manual annotation, improves generalizability, and enhances low-level task performance, using a novel dataset and a lightweight transformation function.

Contribution

The work presents an unsupervised exposure correction approach trained on emulated ISP data, with a new radiometry dataset and a parameter-efficient transformation that outperforms supervised methods.

Findings

Outperforms state-of-the-art supervised methods in exposure correction

Improves downstream tasks like edge detection under poor exposure

Uses only 0.01% of parameters compared to supervised counterparts

Abstract

Current exposure correction methods have three challenges, labor-intensive paired data annotation, limited generalizability, and performance degradation in low-level computer vision tasks. In this work, we introduce an innovative Unsupervised Exposure Correction (UEC) method that eliminates the need for manual annotations, offers improved generalizability, and enhances performance in low-level downstream tasks. Our model is trained using freely available paired data from an emulated Image Signal Processing (ISP) pipeline. This approach does not need expensive manual annotations, thereby minimizing individual style biases from the annotation and consequently improving its generalizability. Furthermore, we present a large-scale Radiometry Correction Dataset, specifically designed to emphasize exposure variations, to facilitate unsupervised learning. In addition, we develop a transformation function that preserves image details and outperforms state-of-the-art supervised methods [12], while utilizing only 0.01% of their parameters. Our work further investigates the broader impact of exposure correction on downstream tasks, including edge detection, demonstrating its effectiveness in mitigating the adverse effects of poor exposure on low-level features. The source code and dataset are publicly available at https://github.com/BeyondHeaven/uec_code.