Defocus Video Matting

Video matting is the process of pulling a high-quality alpha matte and foreground from a video sequence. Current techniques require either a known background (e.g., a blue screen) or extensive user interaction (e.g., to specify known foreground and background elements). The matting problem is generally under-constrained, since not enough information has been collected at capture time. We propose a novel, fully autonomous method for pulling a matte using multiple synchronized video streams that share a point of view but differ in their plane of focus. The solution is obtained by directly minimizing the error in filter-based image formation equations, which are over-constrained by our rich data stream. Our system solves the fully dynamic video matting problem without user assistance: both the foreground and background may be high frequency and have dynamic content, the foreground may resemble the background, and the scene is lit by natural (as opposed to polarized or collimated) illumination.Engineering and Applied Science

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

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

DeOccNet: Learning to See Through Foreground Occlusions in Light Fields

Background objects occluded in some views of a light field (LF) camera can be seen by other views. Consequently, occluded surfaces are possible to be reconstructed from LF images. In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet). In our method, sub-aperture images (SAIs) are first given to the encoder to incorporate both spatial and angular information. The encoded representations are then used by the decoder to render an occlusionfree center-view SAI. To the best of our knowledge, DeOccNet is the first deep learning-based LF-DeOcc method. To handle the insufficiency of training data, we propose an LF synthesis approach to embed selected occlusion masks into existing LF images. Besides, several synthetic and realworld LFs are developed for performance evaluation. Experimental results show that, after training on the generated data, our DeOccNet can effectively remove foreground occlusions and achieves superior performance as compared to other state-of-the-art methods. Source codes are available at: https://github.com/YingqianWang/DeOccNet.Comment: 10 pages, 8 figure

Joint Blind Motion Deblurring and Depth Estimation of Light Field

Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods

Depth and Deblurring from a Spectrally-varying Depth-of-Field

We propose modifying the aperture of a conventional color camera so that the effective aperture size for one color channel is smaller than that for the other two. This produces an image where different color channels have different depths-of-field, and from this we can computationally recover scene depth, reconstruct an all-focus image and achieve synthetic re-focusing, all from a single shot. These capabilities are enabled by a spatio-spectral image model that encodes the statistical relationship between gradient profiles across color channels. This approach substantially improves depth accuracy over alternative single-shot coded-aperture designs, and since it avoids introducing additional spatial distortions and is light efficient, it allows high-quality deblurring and lower exposure times. We demonstrate these benefits with comparisons on synthetic data, as well as results on images captured with a prototype lens.Engineering and Applied Science