Because the micro lenses segment the plenoptic image physically, the global optimization method is hard to be applied on the depth estimation progress and the WTA (winner-take-all) method based on the multibaseline system will generate very coarse but continuous depth map. The global optimizations method always generate robust but concrete depth map when using multi-label technique. So cost projection strategy by directly processing the lenslet plenoptic image is proposed to build the cost volume based on the projection image for global optimization. A method based on Markov Random Fields Propagation (MRFsP) is adopted by merging the modified WTA depth result to refine the concrete depth map. For validation, the depth map built with the proposed method is compared with the microlens-based optimization result, and the result shows obvious improvement.

In order to improve the pixel distinguishability, this paper introduces an algorithm that the quantized geometric relationship among sub images of light field replaced the pixel value. Based on this relationship, the epipolar line division ratio is calculated to take place of pixel value. Experimental results showed that the disparity map with the division ratio images are obviously superior to those obtained by using the subaperture images.

In order to reduce the time consumption of UAV video image dense matching, and improve processing efficiency, this paper presents a fast feature description and feature matching method. The method uses Harris operator to detect corners, adopts feature descriptor S-DASIY (DASIY) to characterize detected corners and generate the 25-dimensional feature descriptor for the corners. The feature points of the images are matched in accordance with the appropriate matching criteria, to get the match points between images. Experimental results show that the proposed fast feature description and feature matching method can significantly reduce the time of generating and matching feature points.