Low-Light Aerial Image Enhancement Algorithm Based on Retinex Theory

Abstract

The existing low-light image enhancement algorithms are ineffective for UAV aerial images due to challenges such as mountain shading and insufficient illumination during cloud imaging. To overcome these challenges, we propose a reflection image enhancement algorithm based on the principles of Retinex theory, integrating illumination estimation networks and attention mechanisms. This approach improves the quality of aerial images with uneven illumination while avoiding overexposure. We employ a lightweight convolutional neural network to estimate low-light image illumination and enhance reflection images. Furthermore, we incorporate an attention mechanism into the reflection image enhancement process to mitigate overexposure and reduce noise. Experimental results confirm the effectiveness of the proposed image enhancement method in mitigating image degradation caused by mountain shading and inadequate illumination during cloud imaging, consequently improving the quality of UAV-captured aerial images.