Retinex-based enhancement study of low illumination blurred road pictures

China has a vast number of roads. With years of use and damage from severe weather and natural disasters, the service life of China’s roads is gradually being shortened. As a result, various road diseases, such as potholes, peeling, cracks, and more, will appear on the road surface. During the collection of road disease datasets, some exceptional cases are inevitable. For example, in low-illumination conditions or highly dark scenes, the visibility of the surrounding environment is very low, significantly reducing the image acquisition quality. Secondly, when the vehicle-mounted camera passes over uneven ground during the shooting process, the vehicle will experience severe jolts, causing the camera to shake irregularly. When the vehicle speed is unstable, the object motion will cause motion blur due to defocusing, which seriously degrades image quality and hurts subsequent advanced visual tasks such as object detection based on road dataset images. Hence, this study embraces a Retinex-based image optimization approach explicitly tailored to enhance the clarity of low-light, blurred road imagery. It has broad practical application prospects for road disease detection in low-illumination blurred environments.