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

BACKGROUND AND OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Face Detection-Induced Access Control System via Large Margin Metric Learning

With the development of science and technology and the acceleration of economic integration, identity authentication has become the most basic element in cyberspace and the basis of the whole information security system. Biometric recognition technology is an important technology in the process of identity authentication. Among them, face recognition technology has been favored by researchers, social applications, and users in the field of identity authentication by virtue of its inherent advantages such as ease of use and insensitivity. In this paper, a face recognition-based access control system is established with the help of large margin metric learning. First, a face library is input into a deep neural network to extract representation features. Second, the deep representation features are used to learn a large margin metric learning model. Third, the face image is captured by a digital camera to input into large margin metric learning model for identifying the person. The experimental results show that the proposed system can accurately identify most of the persons.