NDLI: Toward a general model for reflection recovery and single image enhancement

Content Provider	IET Digital Library
Author	Chang, Meng Li, Qi He, Zhuang Feng, Huajun Xu, Zhihai
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.
Starting Page	3117
Ending Page	3126
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1175
Journal	IET Image Processing
Publisher Date	2020-06-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bright Boundaries Complex Imaging Condition Computer Vision And Image Processing Technique Contrast Enhancement Dark Boundaries Daylight Condition Haze Imaging Model High-light Condition Image Colour Analysis Image Contrast Image Details Image Enhancement Low Light Low-light Condition Optical, Image And Video Signal Processing Reflection Recovery Retinex Model Single Image Enhancement Visual Quality
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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