Joint Retinex-based variational model and CLAHE-in-CIELUV for enhancement of low-quality color retinal images.

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.

Both speckle reduction and contrast enhancement for optical coherence tomography via sequential optimization in the logarithmic domain based on a refined Retinex model.

Optical coherence tomography (OCT) image enhancement is a challenging task because speckle reduction and contrast enhancement need to be addressed simultaneously and effectively. We present a refined Retinex model for guidance in improving the performance of enhancing OCT images accompanied by speckle noise; a physical explanation is provided. Based on this model, we establish two sequential optimization functions in the logarithmic domain for speckle reduction and contrast enhancement, respectively. More specifically, we obtain the despeckled image of an entire OCT image by solving the first optimization function. Incidentally, we can recover the speckle noise map through removing the despeckle component directly. Then, we estimate the illumination and reflectance by solving the second optimization function. Further, we apply the contrast-limited adaptive histogram equalization algorithm to adjust the illumination, and project it back to the reflectance for achieving contrast enhancement. Experimental results demonstrate the robustness and effectiveness of our proposed method. It performs well in both speckle reduction and contrast enhancement and is superior to the other two methods both in terms of qualitative analysis and quantitative assessment. Our method has the practical potential to improve the accuracy of manual screening and computer-aided diagnosis for retinal diseases.