A hybrid method for improving the luminosity and contrast of color retinal images using the JND model and multiple layers of CLAHE

Retinal imaging can be used to identify a variety of common eye and cardiac disorders. However, owing to non-uniform or poor illumination and low contrast, low-quality retinal fundus medical images are ineffective for diagnostic, particularly in computerized image analysis systems. The article proposes an effective image enhancement method for improving the luminosity and contrast of color retinal fundus images. To begin, the input color retinal fundus image is transformed to HSV (Hue, Saturation, and Value) color model, which separates the luminance channel (V) from the other color elements hue (H) and saturation (S). Then, on the luminosity channel (V), a new JND-based adaptive gamma correction method is utilized to improve the luminance of fundus images. After that, contrast is improved in the luminance component in the L*a*b* color space, employing a novel contrast enhancement technique that employs several layers of CLAHE (contrast limited adaptive histogram equalization). These two techniques substantially improve the overall luminance and contrast in retinal images while preserving the average brightness, keeping an original appearance, and maximizing the entropy of the input retinal fundus images. Experiments on a broad range of fundus images are performed to assess the proposed scheme's performance both qualitatively and quantitatively. Substantial objective evaluation indicates that the proposed scheme surpasses state-of-the-art enhancement techniques in terms of edge preservation index, entropy, a measure of enhancement, contrast ratio, and enhancement metrics. This retinal fundus image enhancement method can be employed to support ophthalmologists in effectively inspecting for retinal disorders and developing more accurate computerized image analysis for medical diagnosis.

Automated summary

The article proposes an effective image enhancement method for improving the luminosity and contrast of low-quality retinal fundus images. The method involves color model transformation, luminance correction, and contrast enhancement, and it has been experimentally proven to outperform state-of-the-art techniques.