Joint Over and Under Exposures Correction by Aggregated Retinex Propagation for Image Enhancement
Long Ma, Dian Jin, Risheng Liu, Xin Fan, Zhongxuan Luo · IEEE Signal Processing Letters · 2020
Since the interference of ambient light and the limitation of physical devices, it is quite a common phenomenon that images taken in real-world scenarios turn out to be incorrectly exposed. Most existing techniques emphasize underexposed image correction. On one hand, these works ignore the correction of over-exposure regions in the original input. On the other hand, it is likely to generate over-exposure images. To mitigate these issues, we have developed a novel aggregated Retinex propagations to simultaneously correct over and under-exposure correction of a single image. Concretely, we first manifest the necessity of concurrently correcting under and over-exposure appearances. We establish a Retinex image propagation framework with shared weights to correct different levels of exposure. Then by introducing the fusion computational module, we achieve the accurate exposure correction for a single image. Plenty of quantitative and qualitative comparisons are conducted to fully indicate our superiority against other state-of-the-art algorithms. The elaborated algorithmic analyses show our effectiveness. Experiments on face detection further verify our practicability.