Regularized depth from defocus

In 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.

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Vinay P. Namboodiri, Subhasis Chaudhuri, Sunil Hadap
• Keywords: Depth Estimation, Graph-cuts Based Techniques, ICIP 2008, Image Processing, Regularized Depth