Toward a general model for reflection recovery and single image enhancement. Issue 13 (5th October 2020)

• Record Type: Journal Article
• Authors: Chang, Meng; Li, Qi; He, Zhuang; Feng, Huajun; Xu, Zhihai
• Is Part Of: IET image processing. Volume 14:Issue 13(2020)
• Page Start: 3117
• Page End: 3126
• Publication Date: 2020-10-05
• Subjects: image enhancement -- image colour analysis; image details -- high‐light conditions -- low‐light conditions -- dark boundaries -- bright boundaries -- reflection recovery -- single image enhancement -- visual quality -- low light -- haze imaging model -- contrast enhancement -- daylight condition -- retinex model -- complex imaging conditions -- image contrast; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1175
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Images often suffer from low visual quality due to poor imaging conditions such as low light or hazy weather. The haze imaging model is widely used in contrast enhancement in daylight condition with haze, while the retinex model is universal for low‐light conditions. Although their forms and applications are different, they can be unified into a more general form through the proposed observation. Based on this model, the authors can estimate the reflection of the scene more accurately in more complex imaging conditions. In this study, the authors propose a simple but effective method for estimating the reflection and enhancing the image contrast based on a general imaging model. To preserve the image details and control contrast, the authors introduce dark boundary and bright boundary to handle the high‐light and low‐light conditions, and a guided structure‐preserving optimization algorithm is proposed to estimate them. After obtaining the dark and bright boundaries, the reflection is calculated and the image is enhanced accordingly. Different from previous approaches, which were designed for specific applications, the proposed method can be used for more diverse imaging conditions. Experiments show that the proposed method can be applied to many poor imaging conditions and maintain good performance.