Multi-Array Camera Disparity Enhancement
Multi-array camera systems have greater potential for 3-D depth-based application development compared with stereo camera systems. However, there are very few research results on multi-array-based disparity enhancement, extending standard stereo matchings to multi-array systems. In this paper, we propose to alternately use local and global fusion of multi-array disparities to maximize the disparity enhancement in array camera systems. We propose a new cascade regularization based approach, which can restore diagonal structures better than conventional techniques. The detailed analysis and experimental results verify that the cascade approach better regularizes the diagonal variations and in turn yields better image enhancement. We adapt total variation for regularization to the multi-array camera systems in order to globally combine multiple disparity estimates. A local multiple cross-filling algorithm is proposed to achieve cross consistency between array disparity estimates by effectively filling the mismatches. Experimental results show that the proposed multi-array disparity enhancement algorithm can improve the accuracy of the initial array disparity estimates up to 65% while alleviating memory limitation.