Joint Retinex-based variational model and CLAHE-in-CIELUV for enhancement of low-quality color retinal images
| Autor: | Chen Tang, Min Xu, Zongheng Huang, Zhenkun Lei |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
genetic structures
Computer science media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Datasets as Topic Color space Sensitivity and Specificity 01 natural sciences Retina 010309 optics chemistry.chemical_compound Optics Distortion Image Interpretation Computer-Assisted 0103 physical sciences medicine Humans Contrast (vision) Computer vision Electrical and Electronic Engineering Engineering (miscellaneous) ComputingMethodologies_COMPUTERGRAPHICS media_common Color constancy business.industry Reproducibility of Results Wavelet transform Retinal Image Enhancement medicine.disease Atomic and Molecular Physics and Optics chemistry Gamma correction CIELUV Adaptive histogram equalization Artificial intelligence business Algorithms Retinopathy |
| Zdroj: | Applied Optics. 59:8628 |
| ISSN: | 2155-3165 1559-128X |
| DOI: | 10.1364/ao.401792 |
| Popis: | Poor visual quality of color retinal images greatly interferes with the analysis and diagnosis of the ophthalmologist. In this paper, we propose an enhancement method for low-quality color retinal images based on the combination of the Retinex-based enhancement method and the contrast limited adaptive histogram equalization (CLAHE) algorithm. More specifically, we first estimate the illumination map of the entire image by constructing a Retinex-based variational model. Then, we restore the reflectance map by removing the illumination modified by Gamma correction and directly enable the reflectance as the initial enhancement. To further enhance the clarity and contrast of blood vessels while avoiding color distortion, we apply CLAHE on the luminance channel in CIELUV color space. We collect 60 low-quality color retinal images as our test dataset to verify the reliability of our proposed method. Experimental results show that the proposed method is superior to the other three related methods, both in terms of visual analysis and quantitative evaluation while testing on our dataset. Additionally, we apply the proposed method to four publicly available datasets, and the results show that our methods may be helpful for the detection and analysis of retinopathy. |
| Databáze: | OpenAIRE |
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