Single-Shot Retinal Image Enhancement Using Untrained and Pretrained
Neural Networks Priors Integrated with Analytical Image Priors
Abstract
Retinal images acquired using fundus cameras are often visually blurred
due to imperfect imaging conditions, refractive medium turbidity, and
motion blur. In addition, ocular diseases such as the presence of
cataract also result in blurred retinal images. The presence of blur in
retinal fundus images reduces the effectiveness of the diagnosis process
of an expert ophthalmologist or a computer-aided detection/diagnosis
system. In this paper, we put forward a single-shot deep image prior
(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
while using a single degraded image. To perform retinal image
enhancement, we frame it as a layer decomposition problem and
investigate the use of two well-known analytical priors, i.e., dark
channel prior (DCP) and bright channel prior (BCP) for atmospheric light
estimation. We show that both the untrained neural networks and the
pretrained neural networks can be used to generate an enhanced image
while using only a single degraded image. We evaluate our proposed
framework quantitatively on five datasets using three widely used
metrics and complement that with a subjective qualitative assessment of
the enhancement by two expert ophthalmologists. We have compared our
method with a recent state-of-the-art method cofe-Net using
synthetically degraded retinal fundus images and show that our method
outperforms the state-of-the-art method and provides a gain of 1.23 and
1.4 in average PSNR and SSIM respectively. Our method also outperforms
other works proposed in the literature, which have evaluated their
performance on non-public proprietary datasets, on the basis of the
reported results.