Single-Shot Retinal Image Enhancement Using Deep Image Priors

Abstract

Retinal images acquired using fundus cameras often contain visual artifacts due to imperfect imaging conditions, refractive medium turbidity, and motion blur. In addition, ocular diseases such as the presence of cataract results in blurred retinal image. The presence of these visual artifacts reduces the effectiveness of the diagnosis process followed by an expert ophthalmologist or a computer-aided detection/diagnosis system. In this paper, we put forward a single-shot deep image priors (DIP)-based approach for retinal image enhancement. Unlike typical deep learning-based approaches, our method does not require any training data. Instead, our DIP-based method can learn the underlying image prior using a single degraded image. We show that the architecture of the convolutional neural network imposes a strong image prior that is sufficient for capturing the retinal image statistics to generate an enhanced image using a degraded version of it. We evaluate our proposed framework on five datasets and show that the enhanced images using our proposed method perform significantly better on the retinal image enhancement and synthesis tasks as compared to several competitive baselines.