Color fundus image analysis for detecting the retinal abnormalities requires an improved visualization of image attributes with sufficient luminosity, contrast and accurate edge details. A hybrid technique based on singular value equalization using shearlet transform and adaptive gamma correction, followed by contrast limited adaptive histogram equalization (CLAHE) is proposed for the enhancement of luminosity and contrast in color fundus images. The low frequency components of the original and adaptive gamma transformed value channel in HSV color space obtained by applying shearlet transform are considered for singular value equalization. The high frequency components of the unchanged value channel, denoised using soft thresholding are applied while performing inverse shearlet transform. Luminosity component in $L^{*}{a}^{*}{b}^{*}$ colorspace is considered for performing CLAHE on the singular value equalized image. Subjective analysis is done based on visualization of the image attributes and the objective analysis is carried out based on the parameters such as Peak signal to noise ratio, entropy, feature similarity index, edge-based contrast measure, quality index and noise suppression measure. The simulation results evince superior noise performance, sufficient luminosity adjustment and improved contrast along with excellent edge detail preservation when compared with the existing state-of-the-art techniques.

用于检测视网膜异常的彩色眼底图像分析需要对图像属性进行改进的可视化，并具有足够的亮度，对比度和准确的边缘细节。为了增强彩色眼底图像的亮度和对比度，提出了一种基于奇异值均衡的混合技术，该奇异值均衡使用了剪切波变换和自适应伽马校正，然后进行了对比度受限的自适应直方图均衡（CLAHE）。通过应用小波变换获得的HSV色彩空间中原始和自适应伽马变换值通道的低频分量被考虑用于奇异值均衡。在执行逆小波变换时，将应用使用软阈值去噪的不变值通道的高频分量。中的光度分量${\mathrm{大号}}^{*}{\mathrm{一种}}^{*}{b}^{*}$考虑使用色彩空间对奇异值均等图像执行CLAHE。基于图像属性的可视化进行主观分析，并基于诸如峰信噪比，熵，特征相似性指数，基于边缘的对比度度量，质量指数和噪声抑制度量等参数进行客观分析。与现有技术相比，仿真结果显示出优异的噪声性能，足够的亮度调节和改善的对比度以及出色的边缘细节保留。