Parallax correction via disparity estimation in a multi-aperture camera. Mustaniemi, J., Kannala, J., & Heikkilä, J. Machine Vision and Applications, 27(8):1313-1323, 11, 2016.
Parallax correction via disparity estimation in a multi-aperture camera [link]Website  abstract   bibtex   
In this paper, an image fusion algorithm is proposed for a multi-aperture camera. Such camera is a feasible alternative to traditional Bayer filter camera in terms of image quality, camera size and camera features. The camera consists of several camera units, each having dedicated optics and color filter. The main challenge of a multi-aperture camera arises from the fact that each camera unit has a slightly different viewpoint. Our image fusion algorithm corrects the parallax error between the sub-images using a disparity map, which is estimated from the single-spectral images. We improve the disparity estimation by combining matching costs over multiple views using trifocal tensors. Images are matched using two alternative matching costs, mutual information and Census transform. We also compare two different disparity estimation methods, graph cuts and semi-global matching. The results show that the overall quality of the fused images is near the reference images. © 2016, Springer-Verlag Berlin Heidelberg.
@article{
 title = {Parallax correction via disparity estimation in a multi-aperture camera},
 type = {article},
 year = {2016},
 identifiers = {[object Object]},
 keywords = {Census transform,Graph cuts,Mutual information,Semi-global matching,Trifocal tensor},
 pages = {1313-1323},
 volume = {27},
 websites = {http://link.springer.com/10.1007/s00138-016-0773-7},
 month = {11},
 day = {25},
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 abstract = {In this paper, an image fusion algorithm is proposed for a multi-aperture camera. Such camera is a feasible alternative to traditional Bayer filter camera in terms of image quality, camera size and camera features. The camera consists of several camera units, each having dedicated optics and color filter. The main challenge of a multi-aperture camera arises from the fact that each camera unit has a slightly different viewpoint. Our image fusion algorithm corrects the parallax error between the sub-images using a disparity map, which is estimated from the single-spectral images. We improve the disparity estimation by combining matching costs over multiple views using trifocal tensors. Images are matched using two alternative matching costs, mutual information and Census transform. We also compare two different disparity estimation methods, graph cuts and semi-global matching. The results show that the overall quality of the fused images is near the reference images. © 2016, Springer-Verlag Berlin Heidelberg.},
 bibtype = {article},
 author = {Mustaniemi, Janne and Kannala, Juho and Heikkilä, Janne},
 journal = {Machine Vision and Applications},
 number = {8}
}
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