Aseem Agarwala

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We present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of <i>both</i> blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within(More)
We describe an interactive, computer-assisted framework for combining parts of a set of photographs into a single composite picture, a process we call "digital photomontage." Our framework makes use of two techniques primarily: graph-cut optimization, to choose good seams within the constituent images so that they can be combined as seamlessly as possible;(More)
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as(More)
We describe a technique that transforms a video from a hand-held video camera so that it appears as if it were taken with a directed camera motion. Our method adjusts the video to appear as if it were taken from nearby viewpoints, allowing 3D camera movements to be simulated. By aiming only for perceptual plausibility, rather than accurate reconstruction,(More)
This paper describes a new framework for <i>video matting,</i> the process of pulling a high-quality alpha matte and foreground from a video sequence. The framework builds upon techniques in natural image matting, optical flow computation, and background estimation. User interaction is comprised of garbage matte specification if background estimation is(More)
We describe a new approach to rotoscoping --- the process of tracking contours in a video sequence --- that combines computer vision with user interaction. In order to track contours in video, the user specifies curves in two or more frames; these curves are used as keyframes by a computer-vision-based tracking algorithm. The user may interactively refine(More)
We present a robust and efficient approach to video stabilization that achieves high-quality camera motion for a wide range of videos. In this article, we focus on the problem of transforming a set of input 2D motion trajectories so that they are both smooth and resemble visually plausible views of the imaged scene; our key insight is that we can achieve(More)
One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While these problems can be elegantly expressed in the language of Markov Random Fields (MRF’s), the resulting energy minimization problems(More)
We describe a hierarchical approach to improving the efficiency of <i>gradient-domain compositing</i>, a technique that constructs seamless composites by combining the gradients of images into a vector field that is then integrated to form a composite. While gradient-domain compositing is powerful and widely used, it suffers from poor scalability. Computing(More)
This paper describes a mostly automatic method for taking the output of a single panning video camera and creating a <i>panoramic video texture</i> (PVT): a video that has been stitched into a single, wide field of view and that appears to play continuously and indefinitely. The key problem in creating a PVT is that although only a portion of the scene has(More)