Micro-Lens-Based Matching for Scene Recovery in Lenslet Cameras

Since a light-field camera is able to capture more information than a traditional camera, a lot of methods, such as depth estimation, image super-resolution, and view synthesis, are explored for recovering scene information. In this paper, we propose a novel framework for scene recovery based on lenslet-based light-field camera images. Instead of using traditional matching terms, we design a new micro-lens-based matching term to calculate structure information and recover several kinds of scene information simultaneously. On the one hand, inherent information in micro-lens images is selected to complement details in sub-aperture images. On the other hand, sub-aperture images are used to expand micro-lens images and synthesize new view images. A new micro-lens-based consistency metric is introduced for the matching term to handle occlusions in depth estimation and image reconstruction. The newly appeared and newly occluded areas in synthesized views are analyzed and recovered based on information from surrounding points. Experimental results show that the proposed depth estimation method outperforms state-of-the-art methods on both synthetic and lenslet-based light-field images, especially in low-texture and occlusion regions. Furthermore, the super-resolution and view synthesis methods are able to acquire view images with more details and less aliasing artifacts.