Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photorealistic Scene Reconstruction by Voxel Coloring
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
High performance imaging using large camera arrays
ACM SIGGRAPH 2005 Papers
Background Estimation as a Labeling Problem
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Synthetic Aperture Focusing using a Shear-Warp Factorization of the Viewing Transform
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Natural video matting using camera arrays
ACM SIGGRAPH 2006 Papers
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Reconstructing Occluded Surfaces Using Synthetic Apertures: Stereo, Focus and Robust Measures
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A self-reconfigurable camera array
SIGGRAPH '04 ACM SIGGRAPH 2004 Sketches
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background estimation using graph cuts and inpainting
Proceedings of Graphics Interface 2010
High-Speed videography using a dense camera array
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Image segmentation by iterated region merging with localized graph cuts
Pattern Recognition
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Synthetic aperture imaging using an array of cameras, which has become popular recently, can easily handle the occlusion problem by ''seeing through'' occluders. Unfortunately, the resulting image is still blurry because it combines information not only from the region of interest but also from the occluding regions. Removing the blurriness of synthetic aperture images has become a challenging task for many computer vision applications. In this paper, we propose a novel method to improve the image quality of synthetic aperture imaging using energy minimization. Unlike the conventional synthetic aperture imaging method, which averages images from all the camera views, we reformulate the problem as a labeling problem. In particular, we use the energy minimization method to label each pixel in each camera view to decide whether or not it belongs to an occluder. After that, the focusing at the desired depth is by averaging pixels that are not labeled as occluder. The experimental results show that the proposed method outperforms the traditional synthetic aperture imaging method as well as its improved versions, which are simply dim and blur occluders in the resulting image. To the best of our knowledge, our proposed method is the first one for improving the results of synthetic aperture image without using a training set from the input sequence. As well, it is the first method that makes no assumptions on whether or not the objects in the scenes are static.