Regularized depth from defocus
Published in IEEE International Conference on Image Processing (ICIP), 2008
Recommended citation: V.P. Namboodiri, S. Chaudhuri and S. Hadap (2008). “Regularized depth from defocus”, Proceedings of IEEE International Conference on Image Processing (ICIP) , San Diego, CA, USA, pp. 1520-1523. http://vinaypn.github.io/files/icip08.pdf
n the area of depth estimation from images an interesting approach has been structure recovery from defocus cue. Towards this end, there have been a number of approaches [4,6]. Here we propose a technique to estimate the regularized depth from defocus using diffusion. The coefficient of the diffusion equation is modeled using a pair-wise Markov random field (MRF) ensuring spatial regularization to enhance the robustness of the depth estimated. This framework is solved efficiently using a graph-cuts based techniques. The MRF representation is enhanced by incorporating a smoothness prior that is obtained from a graph based segmentation of the input images. The method is demonstrated on a number of data sets and its performance is compared with state of the art techniques.
Recommended citation: V.P. Namboodiri, S. Chaudhuri and S. Hadap (2008). “Regularized depth from defocus”, Proceedings of IEEE International Conference on Image Processing (ICIP) , San Diego, CA, USA, pp. 1520-1523.