Self-supervision based super-resolution approach for light field refocused image

Abstract

Light field imaging can record spatial and angular information of scenes simultaneously, which can provide images focused at different depths by computational imaging. However, the number of sensor pixels and the size of the microlens array limit the resolution of refocused images, which makes them difficult to be used for downstream tasks. To overcome this limitation, we propose a self-supervised super-resolution algorithm to increase the resolution of refocused images, which relies only on the image prior information. With the prior information of low-resolution refocused images and convolutional structure, we can not only significantly improve image quality, but also solve the problem of insufficient training data. Intensive experiments show that the proposed self-supervised approach is able to obtain impressive results and is even comparable to the data-hungry supervised learning methods.