Exploring Defocus Matting: Nonparametric Acceleration, Super-Resolution, and Off-Center Matting

Defocus matting is a fully automatic and passive method for pulling mattes from video captured with coaxial cameras that have different depths of field and planes of focus. Nonparametric sampling can accelerate the video-matting process from minutes to seconds per frame. In addition, a super-resolution technique efficiently bridges the gap between mattes from high-resolution video cameras and those from low-resolution cameras. Off-center matting pulls mattes for an external high-resolution camera that doesn't share the same center of projection as the low-resolution cameras used to capture the defocus matting data.Engineering and Applied Science

A multi-camera approach to image-based rendering and 3-D/Multiview display of ancient chinese artifacts

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Highlighted depth-of-field photography: Shining light on focus

We present a photographic method to enhance intensity differences between objects at varying distances from the focal plane. By combining a unique capture procedure with simple image processing techniques, the detected brightness of an object is decreased proportional to its degree of defocus. A camera-projector system casts distinct grid patterns onto a scene to generate a spatial distribution of point reflections. These point reflections relay a relative measure of defocus that is utilized in postprocessing to generate a highlighted DOF photograph. Trade-offs between three different projectorprocessing pairs are analyzed, and a model is developed to help describe a new intensity-dependent depth of field that is controlled by the pattern of illumination. Results are presented for a primary single snapshot design as well as a scanning method and a comparison method. As an application, automatic matting results are presented.Alfred P. Sloan Foundatio

Light field image processing: an overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data

Computational Schlieren Photography with Light Field Probes

We introduce a new approach to capturing refraction in transparent media, which we call light field background oriented Schlieren photography. By optically coding the locations and directions of light rays emerging from a light field probe, we can capture changes of the refractive index field between the probe and a camera or an observer. Our prototype capture setup consists of inexpensive off-the-shelf hardware, including inkjet-printed transparencies, lenslet arrays, and a conventional camera. By carefully encoding the color and intensity variations of 4D light field probes, we show how to code both spatial and angular information of refractive phenomena. Such coding schemes are demonstrated to allow for a new, single image approach to reconstructing transparent surfaces, such as thin solids or surfaces of fluids. The captured visual information is used to reconstruct refractive surface normals and a sparse set of control points independently from a single photograph.Natural Sciences and Engineering Research Council of CanadaAlfred P. Sloan FoundationUnited States. Defense Advanced Research Projects Agency. Young Faculty Awar