A Retinex-based variational model for noise suppression and non-uniform illumination correction in corneal confocal microscopy images


 Objective. Corneal confocal microscopy (CCM) image analysis is a non-invasive in vivo clinical technique that can quantify corneal nerve fiber damage. However, the acquired CCM images are often accompanied by speckle noise and non-uniform illumination, which seriously affects the analysis and diagnosis of the diseases. Approach. In this paper, first we propose a variational Retinex model for the inhomogeneity correction and noise removal of CCM images. In this model, the Beppo-Levi (BL) space is introduced to constrain the smoothness of the illumination layer for the first time, and the fractional order differential is adopted as the regularization term to constrain reflectance layer. Then, a denoising regularization term is also constructed with Block Matching 3D (BM3D) to suppress noise. Finally, by adjusting the uneven illumination layer, we obtain the final results. Second, an image quality evaluation metric is proposed to evaluate the illumination uniformity of images objectively. Main results. To demonstrate the effectiveness of our method, the proposed method is tested on 628 low-quality CCM images from the CORN-2 dataset. Extensive experiments show the proposed method outperforms the other four related methods in terms of noise removal and uneven illumination suppression. Significance This demonstrates that the proposed method may be helpful for the diagnostics and analysis of eye diseases.