Abstract
Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they can well solve many technical problems that cannot be done by conventional cameras. An important feature of light field cameras is that a microlens array is inserted between the sensor and main lens, through which a series of sub-aperture images of different perspectives are formed. Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property. This algorithm consists of three steps: 1) Computing the dense optical flow field among a group of sub-aperture images; 2) Obtaining a robust depth-estimation by initializing the light-filed optical flow using the linear regression approach and detecting occluded areas using the consistency; 3) Computing an improved light-field depth map by using the edge-preserving algorithm to realize interpolation optimization. The reliability and high accuracy of the proposed approach is validated by experimental results.
Highlights
Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property
Light Field cameras, due to their ability of collecting 4D light field information, provide many capabilities that can never be provided by conventional cameras, such as controlling of field depth, refocusing after the fact, and changing of view points [1]
Light field cameras enjoy a variety of applications, such like light field depth map estimation [2], digital refocusing [3], 3-D image
Summary
Light Field cameras, due to their ability of collecting 4D light field information, provide many capabilities that can never be provided by conventional cameras, such as controlling of field depth, refocusing after the fact, and changing of view points [1]. Many depth estimation algorithms based on light-field images have been proposed [4]. These algorithms do not take spatial and angular resolution into consideration, having the following two limitations in the presence of depth discontinuities or occlusions. The dense optical flow field of multi-frame sub-apertures of light-field images is computed. Based on the geometrical features of light-field images and the correspondence among them, a robust light-field flow can be obtained, which contains the accurate depth estimation and occlusion detection. It can well handle two problems that are suffered by conventional optical flow approaches: one is the accuracy-loss during the computation of the optical flow value between two images, and the other is that the occlusion region cannot be detected correctly
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