Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well {explored}. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: {$\mathit{\text{While}}\mathit{\text{Low-Light}}\mathit{\text{Image}}\mathit{\text{Enhancement}}\mathit{\text{(LLIE)}}\mathit{\text{methods}}\mathit{\text{enhance}}\mathit{\text{human}}\mathit{\text{visual}}\mathit{\text{interpretation,}}\mathit{\text{their}}\mathit{\text{effect}}\mathit{\text{on}}\mathit{\text{computer}}\mathit{\text{vision}}\mathit{\text{tasks}}\mathit{\text{is}}\mathit{\text{inconsistent}}\mathit{\text{and}}\mathit{\text{can}}\mathit{\text{sometimes}}\mathit{\text{be}}\mathit{\text{harmful.}}$} Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision.