Luminosity rectified blind Richardson-Lucy deconvolution for single retinal image restoration.

• A luminosity rectified Richardson-Lucy deconvolution model is proposed to achieve single-image blind deconvolution and illumination correction for retinal images. • A non-convex cost function based on the double-pass fundus reflection model is designed and is solved using gradient descent with Nesterov accelerated adaptive momentum. • The proposed model estimates the latent image, the illumination pattern, and the blurry kernel simultaneously. • The proposed model is compared against the state-of-the-art methods, showing its superiority in terms of restoration quality and implementation efficiency. • The proposed model improves the clarity of retinal images which benefits clinical diagnosing. Due to imperfect imaging conditions, retinal images can be degraded by uneven/insufficient illumination, blurriness caused by optical aberrations and unintentional motions. Degraded images reduce the effectiveness of diagnosis by an ophthalmologist. To restore the image quality, in this research we propose the luminosity rectified Richardson-Lucy (LRRL) blind deconvolution framework for single retinal image restoration. We established an image formation model based on the double-pass fundus reflection feature and developed a differentiable non-convex cost function that jointly achieves illumination correction and blind deconvolution. To solve this non-convex optimization problem, we derived the closed-form expression of the gradients and used gradient descent with Nesterov-accelerated adaptive momentum estimation to accelerate the optimization, which is more efficient than the traditional half quadratic splitting method. The LRRL was tested on 1719 images from three public databases. Four image quality matrixes including image definition, image sharpness, image entropy, and image multiscale contrast were used for objective assessments. The LRRL was compared against the state-of-the-art retinal image blind deconvolution methods. Our LRRL corrects the problematic illumination and improves the clarity of the retinal image simultaneously, showing its superiority in terms of restoration quality and implementation efficiency. The MATLAB code is available on Github. [ABSTRACT FROM AUTHOR]