Procedures for optimization problems with a mixture of bounds and general linear constraints
ACM Transactions on Mathematical Software (TOMS)
A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from Defocus vs. Stereo: How Different Really Are They?
International Journal of Computer Vision - Special issue on computer vision research at the Technion
Robot Vision
Optimal Selection of Camera Parameters for Recovery of Depth from Defocused Images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Depth Measurement by the Multi-Focus Camera
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The Optimal Axial Interval in Estimating Depth from Defocus
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Geometric Approach to Shape from Defocus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Analyzing depth from coded aperture sets
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring
International Journal of Computer Vision
Defocus map estimation from a single image
Pattern Recognition
Single image blind deconvolution with higher-order texture statistics
Proceedings of the 2010 international conference on Video Processing and Computational Video
Perceptually Optimized Coded Apertures for Defocus Deblurring
Computer Graphics Forum
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The finite depth of field of a real camera can be used to estimate the depth structure of a scene. The distance of an object from the plane in focus determines the defocus blur size. The shape of the blur depends on the shape of the aperture. The blur shape can be designed by masking the main lens aperture. In fact, aperture shapes different from the standard circular aperture give improved accuracy of depth estimation from defocus blur. We introduce an intuitive criterion to design aperture patterns for depth from defocus. The criterion is independent of a specific depth estimation algorithm. We formulate our design criterion by imposing constraints directly in the data domain and optimize the amount of depth information carried by blurred images. Our criterion is a quadratic function of the aperture transmission values. As such, it can be numerically evaluated to estimate optimized aperture patterns quickly. The proposed mask optimization procedure is applicable to different depth estimation scenarios. We use it for depth estimation from two images with different focus settings, for depth estimation from two images with different aperture shapes as well as for depth estimation from a single coded aperture image. In this work we show masks obtained with this new evaluation criterion and test their depth discrimination capability using a state-of-the-art depth estimation algorithm.