Retinal fundus image superresolution generated by optical coherence tomography based on a realistic mixed attention GAN.

PURPOSE

Optical coherence tomography (OCT) is widely used to diagnose retinal diseases. However, due to the limited resolution of OCT imaging systems, the quality of fundus images displayed is not satisfactory, which hinders the diagnosis of patients by ophthalmologists. This is an inevitable problem of OCT imaging systems, but few people have given attention to it. We attempt to solve this problem through deep learning methods.

METHODS

In this paper, we propose a single-image superresolution (SISR) model that is based on a generative adversarial network (GAN) for restoring low-resolution (LR) OCT fundus images to high-resolution (HR) counterparts. To obtain more realistic images, we craft the training data set by obtaining the real blur kernels of the LR images instead of using the bicubic interpolation kernel. The baseline of our generator is similar to that of an enhanced superresolution generative adversarial network (ESRGAN), but we creatively propose a mixed attention block (MAB). In contrast to other superresolution (SR) tasks, to adapt to the characteristics of OCT imaging systems, our network can reconstruct LR images with different upscaling factors in the height and width directions.

RESULTS

The results of qualitative and quantitative experiments prove that our model is capable of reconstructing retinal fundus images clearly and accurately.

CONCLUSIONS

We propose a new GAN model for enhancing the quality of displayed OCT retinal fundus images and achieve state-of-the-art results.