Devignetting fundus images via Bayesian estimation of illumination component and gamma correction

摘要

Background: Fundus photography is an imaging modality exclusively used in ophthalmology for visualizing structures like macula, retina, and optic disc. The fundus camera has only one illumination source, which is situated at its center. Hence, structures away from the center will appear darker than naturally they are. This adverse effect caused by the uneven illumination is called as 'vignetting'. Objectives: An algorithm termed as Gamma Correction of Illumination Component (GCIC) for devignetting fundus images is proposed in this paper. Methods: Inspired by the Retinex theory, the illumination component is computed with the help of a Maximum a Posteriori (MAP) estimator. The estimated illumination component after normalization is subjected to the Gamma correction to suppress its unevenness. Results: GCIC exhibited comparatively low values of Average Gradient of the Illumination Component (AGIC), Lightness Order Error (LOE), and computational time. The proposed method gave a comparatively better performance in terms of the performance metrics, namely contrast-to-noise-ratio (CNR), peak-signal-to-noise-ratio (PSNR), structure similarity index (SSIM), and entropy. With respect to the cumulative performance, GCIC has been observed to be better than other devignetting algorithms in the literature, like Illumination Equalization model, Homomorphic Filtering, Adaptive Gamma Correction (AGC), Modified Sigmoid Transform (MST), Imran Qureshi et al. (2019), Zheng et al., Variation-based Fusion (VF) and Zhou's et al. Conclusion: GCIC corrects the uneven background illumination without scaling or boosting it intolerably. It produces output images, which are natural in appearance, free from color artefacts, and maintaining the sharpness of the fundus features. It is computationally fast as well.