Data-driven enhancement of blurry retinal images via generative adversarial networks

In this paper, we aim at improving the quality of blurry retinal images that are caused by ocular diseases. The blurry images could affect clinical diagnosis for both ophthalmologists and automatic aided system. Inspired by the great success of generative adversarial networks, a data-driven approach is proposed to enhance the blurry images in a weakly supervised manner. That is to say, instead of paired blurry and high-quality images, our approach can be trained with two sets of unpaired images. The advantage of unpaired training setting makes our approach easily applicable, since the annotated data are very limited in medical images. Compared with traditional methods, our model is an end-to-end approach without human designed adjustments or prior knowledge. However, it achieves a superior performance on blurry images. Besides, a dynamic retinal image feature constraint is proposed to guide the generator to improve the performance and avoid over-enhancing the extremely blurry region. Our approach can work on large image resolution which makes it widely beneficial to clinic images.