LIME-Eval: Rethinking Low-light Image Enhancement Evaluation via Object Detection
This addresses the need for more robust evaluation metrics in low-light image enhancement, which is crucial for applications like autonomous driving and surveillance, though it is incremental as it builds on existing high-level task evaluation approaches.
The paper tackles the problem of unreliable evaluation in low-light image enhancement by demonstrating that current object detection-based metrics are prone to overfitting, and proposes LIME-Eval, a novel framework that uses pre-trained detectors without annotations to assess enhancement quality, showing effectiveness through comprehensive experiments.
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.