Retinex-based image enhancement framework by using region covariance filter
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Clear images are critical in understanding real scenarios. However, the quality of images may be severely declined due to terrible conditions. Images exposed to such conditions are usually of low contrast, contain much noise, and suffer from weak details. And these drawbacks tend to negatively influence the subsequent processing tasks. Many existing image enhancement methods only solve a certain aspect of aforementioned drawbacks. This paper proposes a Retinex-based image enhancement framework that can increase contrast, eliminate noise, and enhance details at the same time. First, we utilize a region covariance filter to estimate the illumination accurately at multiple scales. The corresponding reflectance can be predicted by dividing the original image by its illumination. Second, we utilize contrast-limited adaptive histogram equalization to enhance the global contrast of original images because the illumination contains the low-frequency component. Third, since the reflectance contains the details of the original image and noise, we adopt a non-local means filter to eliminate noise and use a guided filter to enhance the details in the reflectance. Fourth, we synthesize the final enhanced image by fusing the enhanced illumination and reflectance at each scale. Experiments have proved the improvement of the proposed framework in terms of both visual perception and quantitative comparisons with other compared methods.
KeywordsRetinex Image enhancement Multiple scales Illumination estimation
The study was funded by the National Natural Science Foundation of China (No. 61711540303), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) Fund.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
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