Blind-Depth Light Field Super-Resolution

Abstract

Light field cameras have drawn much attention due to the ability of post-capture adjustments such as refocusing and 3D reconstruction. However, the low resolution has been the bottleneck of them and limits their further application. To solve this problem, various methods have been proposed to increase the resolution, namely, to recover details and reconstruct a high resolution (HR) image of the scene from several low resolution (LR) observations. Most of the previous super-resolution (SR) frameworks depend on prior knowledge of depth information, which is an indispensable part in their SR approaches. However, it may be difficult to obtain precise depth information in some practical situations. In this paper, we propose a novel light field super-resolution (LFSR) framework independent of the prior depth information. The framework combines a variational regularization-based SR approach with light field refocusing process, which can super-resolve the focused region while preserve the unfocused region and generate a series of multi-focus super-resolved images. We then employ a multi-focus image fusion algorithm based on stationary wavelet transform (SWT) and finally obtain an all-in-focus HR image. Synthetic and real-world datasets are utilized to demonstrate the effectiveness of the proposed framework both quantitatively and visually.