LIME-Eval: Rethinking Low-light Image Enhancement Evaluation via Object Detection

TL;DR

LIME-Eval introduces a new evaluation framework for low-light image enhancement that avoids overfitting issues and does not rely on object annotations, using an energy-based strategy and human preference data for more reliable assessment.

Contribution

The paper proposes LIME-Eval, a novel evaluation method that bypasses detector retraining and annotation reliance, and introduces LIME-Bench, an online platform for correlating human preferences with automated metrics.

Findings

LIME-Eval effectively assesses enhancement quality without retraining detectors.

LIME-Bench provides a dataset linking human preferences to evaluation metrics.

Experiments show LIME-Eval's superior reliability over traditional detector-based methods.

Abstract

Due to the nature of enhancement--the absence of paired ground-truth information, high-level vision tasks have been recently employed to evaluate the performance of low-light image enhancement. A widely-used manner is to see how accurately an object detector trained on enhanced low-light images by different candidates can perform with respect to annotated semantic labels. In this paper, we first demonstrate that the mentioned approach is generally prone to overfitting, and thus diminishes its measurement reliability. In search of a proper evaluation metric, we propose LIME-Bench, the first online benchmark platform designed to collect human preferences for low-light enhancement, providing a valuable dataset for validating the correlation between human perception and automated evaluation metrics. We then customize LIME-Eval, a novel evaluation framework that utilizes detectors pre-trained on standard-lighting datasets without object annotations, to judge the quality of enhanced images. By adopting an energy-based strategy to assess the accuracy of output confidence maps, our LIME-Eval can simultaneously bypass biases associated with retraining detectors and circumvent the reliance on annotations for dim images. Comprehensive experiments are provided to reveal the effectiveness of our LIME-Eval. Our benchmark platform (https://huggingface.co/spaces/lime-j/eval) and code (https://github.com/lime-j/lime-eval) are available online.