Abstract:
Methods and apparatus for describing a projection model, used by a panoramic image stitching module to generate panoramic images and for communicating the projection model to other processes. A post-processing module may access and use the projection model provided by the panoramic image stitching module to perform one or more post-processing methods on the panoramic image, rather than requiring the user to input the projection model via a user interface or requiring the post-processing module to estimate the projection model according to a mathematical analysis of the panoramic image.
Abstract:
Embodiments of the present invention provide systems, methods, and computer storage media directed towards automatic selection of regions for blur kernel estimation. In one embodiment, a process divides a blurred image into a regions. From these regions a first region and a second region can be selected based on a number of edge orientations within the selected regions. A first blur kernel can then be estimated based on the first region and a second blur kernel can be estimated for the second region. The first and second blur kernel can then be utilized to respectively deblur a first and second portion of the image to produce a deblurred image. Other embodiments may be described and/or claimed.
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 computer-implemented method and apparatus are described for deblurring an image. The method may include causing display of a graphical user interface configured to be used to deblur the image. The graphical user interface may include a display zone and a control zone adjacent to the display zone. A user-selected image may be displayed in the display zone, and a suggested blur kernel may be displayed in the control zone, wherein the blur kernel is associated with a blurred region in the user-selected image. The suggested blur kernel is then displayed proximate the associated blurred region in the display zone.
Abstract:
Embodiments of the present invention provide systems, methods, and computer storage media directed towards automatic selection of regions for blur kernel estimation. In one embodiment, a process divides a blurred image into a regions. From these regions a first region and a second region can be selected based on a number of edge orientations within the selected regions. A first blur kernel can then be estimated based on the first region and a second blur kernel can be estimated for the second region. The first and second blur kernel can then be utilized to respectively deblur a first and second portion of the image to produce a deblurred image. Other embodiments may be described and/or claimed.
Abstract:
A computer-implemented method and apparatus are described for deblurring an image. The method may include causing display of a graphical user interface configured to be used to deblur the image. The graphical user interface may include a display zone and a control zone adjacent to the display zone. A user-selected image may be displayed in the display zone, and a suggested blur kernel may be displayed in the control zone, wherein the blur kernel is associated with a blurred region in the user-selected image. The suggested blur kernel is then displayed proximate the associated blurred region in the display zone.
Abstract:
A computer-implemented method and apparatus are described for automatically selecting a region in a blurred image for blur kernel estimation. The method may include accessing a blurred image and defining a size for each of a plurality of regions in the image. Thereafter, metrics for at least two of the plurality of regions are determined, wherein the metrics are based on a number of edge orientations within each region. A region is selected from the plurality of regions based on the determined metrics, and a blur kernel for deblurring the blurred image is then estimated for the selected region. The blurred image is then deblurred using the blur kernel.
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 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 computer-implemented method and system are described for deblurring an image. The method may include accessing an image having a first blurred region and a second blurred region, and generating a first blur kernel for the first blurred region and a second blur kernel for the second blurred region. Thereafter, the first blur kernel is positioned with respect to the first blurred region, and the second blur kernel is positioned with respect to the second blurred region based on the position of the first blur kernel. The image is then deblurred by deconvolving the first blurred region with the first blur kernel, and deconvolving the second blurred region with the second blur kernel.