This paper presents a computational framework for accurately estimating the disparity map of plenoptic images. The proposed framework is based on the variational principle and provides intrinsic sub-pixel precision. The light-field motion tensor introduced in the framework allows us to combine advanced robust data terms as well as provides explicit treatments for different color channels. A warping strategy is embedded in our framework for tackling the large displacement problem. We also show that by applying a simple regularization term and a guided median filtering, the accuracy of displacement field at occluded area could be greatly enhanced. We demonstrate the excellent performance of the proposed framework by intensive comparisons with the Lytro software and contemporary approaches on both synthetic and real-world datasets.
Compared with previous works, especially variational approaches @cite_11 @cite_6 , our contribution is two-fold. Firstly, we introduce a light-field motion tensor that allows us to take advantages of different constancy assumptions and explicitly define the contribution of different color channels. Secondly, we proposed to enhance the discontinues of disparity field at occluded areas by appropriately applying guided median filtering. Experimental results show the great accuracy achievement when compared with previous approaches.
@cite_6 @cite_11
109257483 2042687553
In this paper we propose an efficient method to calculate a high-quality depth map from a single raw image captured by a light field or plenoptic camera. The proposed model combines the main idea of Active Wavefront Sampling AWS with the light field technique, i.e. we extract so-called sub-aperture images out of the raw image of a plenoptic camera, in such a way that the virtual view points are arranged on circles around a fixed center view. By tracking an imaged scene point over a sequence of sub-aperture images corresponding to a common circle, one can observe a virtual rotation of the scene point on the image plane. Our model is able to measure a dense field of these rotations, which are inversely related to the scene depth. We develop a continuous framework for the analysis of 4D light fields, and describe novel variational methods for disparity reconstruction as well as spatial and angular super-resolution. Disparity maps are estimated locally using epipolar plane image analysis without the need for expensive matching cost minimization. The method works fast and with inherent subpixel accuracy since no discretization of the disparity space is necessary. In a variational framework, we employ the disparity maps to generate super-resolved novel views of a scene, which corresponds to increasing the sampling rate of the 4D light field in spatial as well as angular direction. In contrast to previous work, we formulate the problem of view synthesis as a continuous inverse problem, which allows us to correctly take into account foreshortening effects caused by scene geometry transformations. All optimization problems are solved with state-of-the-art convex relaxation techniques. We test our algorithms on a number of real-world examples as well as our new benchmark data set for light fields, and compare results to a multiview stereo method. The proposed method is both faster as well as more accurate. Data sets and source code are provided online for additional evaluation.