Abstract:
A computer-implemented method and apparatus are described for deblurring an image. The method may include accessing the image that has at least one blurred region and, automatically, without user input, determining a first value for a first size for a blur kernel for the at least one blurred region. Thereafter, automatically, without user input, a second value for a second size for the blur kernel is determined for the at least one blurred region. A suggested size for the blur kernel is then determined based on the first value and the second value.
Abstract:
A blurred image having a spatially invariant motion blur resulting from camera motion during image capture is deblurred based on one or more light streaks identified and extracted from the blurred image. A blur kernel for the blurred image is estimated by performing an optimization procedure having a blur kernel constraint based at least in part on the light streak. One or more light streaks can in some embodiments be posed as the blur kernel constraint. A modeled light streak may be defined as a convolution between the blur kernel and a simulated light source, with the optimization procedure being to minimize a distance between the modeled light streak and the corresponding identified light streak from the blurred image.
Abstract:
Methods for enhancing images with increased efficiency include using a discriminative index tree to expedite image optimization processes. The discriminative index tree indexes patch-based image priors for modifying an image by using classifiers determined by exploiting a structure of the patch-based image priors. The discriminative index tree quickly and efficiently parses a space of patch-based image patches to determine approximate dominant patch-based image priors for the space of image patches. To further improve the efficiency of the discriminative index tree, one or more embodiments can limit a number of potential patch-based image priors from which a dominant patch-based image prior is selected.
Abstract:
An image de-blurring system obtains a blurred input image and generates, based on the blurred input image, a blur kernel. The blur kernel is an indication of how the image capture device was moved and/or how the subject captured in the image moved during image capture, resulting in blur. Based on the blur kernel and the blurred input image, a de-blurred image is generated. The blur kernel is generated based on sharp versions of the blurred input image predicted using a data-driven approach based on a collection of prior edges.
Abstract:
A computer-implemented method and apparatus are described for deblurring an image. The method may include accessing the image that has at least one blurred region and, automatically, without user input, determining a first value for a first size for a blur kernel for the at least one blurred region. Thereafter, automatically, without user input, a second value for a second size for the blur kernel is determined for the at least one blurred region. A suggested size for the blur kernel is then determined based on the first value and the second value.
Abstract:
Systems and methods are provided for providing improved de-noising image content by using directional noise filters to accurately estimate a blur kernel from a noisy blurry image. In one embodiment, an image manipulation application applies multiple directional noise filters to an input image to generate multiple filtered images. Each of the directional noise filters has a different orientation with respect to the input image. The image manipulation application determines multiple two-dimensional blur kernels from the respective filtered images. The image manipulation application generates a two- two-dimensional blur kernel for the input image from the two-dimensional blur kernels for the filtered images. The image manipulation application generates a de-blurred version of the input image by executing a de-blurring algorithm based on the two-dimensional blur kernel for the input image.
Abstract:
Methods for enhancing images with increased efficiency include using a discriminative index tree to expedite image optimization processes. The discriminative index tree indexes patch-based image priors for modifying an image by using classifiers determined by exploiting a structure of the patch-based image priors. The discriminative index tree quickly and efficiently parses a space of patch-based image patches to determine approximate dominant patch-based image priors for the space of image patches. To further improve the efficiency of the discriminative index tree, one or more embodiments can limit a number of potential patch-based image priors from which a dominant patch-based image prior is selected.
Abstract:
Techniques are disclosed for collaborative and synchronized photography across multiple digital camera devices. A panoramic photograph of a scene can be generated from separate photographs taken by each of the cameras simultaneously. During composition, the viewfinder images from each camera are collected and stitched together on the fly to create a panoramic preview image. The panoramic preview is then displayed on the camera devices as live visual guidance, which each user can use to change the orientation of the camera and thus change the composition of the panoramic photograph. In some cases, the host sends visual instructions to other camera devices to guide users in camera adjustment. When the desired composition is achieved, the host sends a trigger command to all of the cameras to take photographs simultaneously. Each of these separate photographs can then be stitched together to form a panoramic photograph.
Abstract:
A simulated tracking shot is generated from an image sequence in which a foreground feature moves relative to a background during capturing of the image sequence. The background is artificially blurred in the simulated tracking shot in a spatially-invariant manner corresponding to foreground motion relative to the background during a time span of the image sequence. The foreground feature can be substantially unblurred relative to a reference image selected from the image sequence. A system to generate the simulated tracking shot can be configured to derive spatially invariant blur kernels for a background portion by reconstructing or estimating a 3-D space of the captured scene, placing virtual cameras along a foreground trajectory in the 3-D space, and projecting 3-D background points on to the virtual cameras.
Abstract:
Joint video deblurring and stabilization techniques are described. In one or more implementations, a deblurring and stabilization module is configured to jointly deblur and stabilize a video by grouping video frames into spatial-neighboring frame clusters, and building local mesh homographies for video frames in each spatial-neighboring frame cluster.