A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Observing Shape from Defocused Images
International Journal of Computer Vision
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Extracting depth and matte using a color-filtered aperture
ACM SIGGRAPH Asia 2008 papers
Regularized image restoration by means of fusion for digital auto focusing
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Multi-object digital auto-focusing using image fusion
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Shape from focus using multilayer feedforward neural networks
IEEE Transactions on Image Processing
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Most of the focusing techniques need to estimate depth information for ensuring that the object of interest is at an appropriate distance for full frontal focus. Computational cameras which can variably focus different regions of the scene with large depth of field have been proposed. In this paper we propose a full autofocusing algorithm using computational camera without involving any digital image restoration methods and just one input. The proposed computational camera uses multiple filter apertures corresponding to each color channel which can acquire three shifted views of a scene in the RGB color planes. We can make any region focused by appropriately shifting each color channel to be aligned. Depth map estimation is carried out to extract different regions from these channel shifted images which is later fused to produce the final image without any focal blur. Experimental results show performance and feasibility of the proposed algorithm for autofocusing images with one or more differently out-of-focused objects.