Thin observation module by bounded optics (TOMBO) is an optical system substituting a micro lens-let array with smaller apertures for a conventional large full aperture. This array allows us to capture multiple low resolution sub-images of the same scene and use them to reconstruct a high resolution image. While lost resolutions can be recovered, there has been very little work on experimentally evaluating restored resolution performance in the TOMBO system. Our work focuses on resolution comparisons among a 4×4 lens-let TOMBO and Nikon lenses in the same f number condition. Experimental results present the equivalent focal length of the experimental TOMBO system.
With a multi-lenslet camera, we can capture multiple low resolution subimages of the same scene and use them to
reconstruct a high resolution image. The spatially variant shifts estimation between subimages is one of major problems. In this paper, a depth estimation algorithm has been proposed for multi-lenslet cameras. The stereo matching between the reference subimage and other subimages using segmentation-based Adaptive Support-Weight approach combined with Scale Invariant Feature Transform (SIFT) is introduced, which has an influence on the result of stereo matching. Then, disparity maps are converted to depth maps and these depth maps are merged into one map for quality improvement. At last, the average blending images at difference depth are calculated according to the depth map. The experimental results show that the proposed algorithm can extract accurate depth more concisely and efficiently.
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