From dim to glow: dynamic illuminance adjustment for simultaneous exposure correction and low-light image enhancement

Abstract

Acquiring high-quality images in diverse lighting conditions is challenging, particularly in the constantly evolving driving landscape. From the harsh brilliance of daylight to the subtle shadows of nighttime scenarios, the complexity of capturing accurate images intensifies. Therefore, addressing exposure issues across various lighting conditions is imperative. This study introduces a novel multi-task end-to-end dynamic illumination adjustment approach termed CPGA-DIA. By incorporating feature selection and gamma factor correction techniques, our method effectively handles extreme variations in light conditions for both low-light image enhancement and exposure correction. Comprehensive experimental evaluations demonstrate superior performance to existing processes, showcasing our method’s adaptability and robustness in real-world driving scenarios. Furthermore, our approach extends its capabilities to include the control and estimation of explainable factors related to changing lightness, enabling our model to understand better the environmental conditions that influence image perception and clarity. This research contributes to advancing image capture methodologies, particularly in dynamic lighting conditions, with promising implications for applications ranging from photography to various other fields.