Abstract: The subject disclosure is directed towards a curved image sensor that is rotated in one or more rotational directions to compensate for camera movement, e.g., camera shake. In one aspect, sensors such as gyroscopes output camera movement data, which a controller uses to rotate the curved (e.g., hemispherical) image sensor about its center of curvature, e.g., via signals to electrical and/or magnetic movement mechanisms. Image processing may be used by the controller to rotate the image sensor.

Abstract: The subject disclosure is directed towards an image sensor that is controllable curved to adapt for differences in lens focal lengths. Variable data such as focal length data, measured curvature data and/or image quality data is received at a curve controller that curves the sensor based upon the variable data. In one aspect, a camera is provided with a lens having a variable focal length and a sensor capable of being dynamically curved. A curve controller receives image quality data and iteratively uses the image quality data to adjust the curvature to attempt to increase the image quality of a subsequent image to be captured.

Abstract: The description relates to enhancing images. One device includes a lens configured to focus images on an image sensor. The device also includes point spread function (PSF) lens data relating to manufacturing specifications of the lens and a PSF measurement of the lens associated with a test image of a planar calibration pattern at a single depth of field.

Abstract: The described implementations relate to enhancing images. One system includes a lens configured to focus an image on an image sensor. The image sensor is configured to capture the image. The model of the lens can be known to the system. The system can also include an image enhancement component configured to receive the captured image and to utilize a simulated blur kernel of the known model to produce an enhanced image.

Abstract: The described implementations relate to deblurring images. One system includes an imaging device configured to capture an image, a linear motion detector and a rotational motion detector. This system also includes a controller configured to receive a signal from the imaging device relating to capture of the image and to responsively cause the linear motion detector and the rotational motion detector to detect motion-related information. Finally, this particular system includes a motion calculator configured to recover camera motion associated with the image based upon the detected motion-related information and to infer imaging device motion induced blur of the image and an image deblurring component configured to reduce imaging device induced blur from the image utilizing the inferred camera motion induced blur.

Abstract: The described implementations relate to deblurring images. One system includes an imaging device configured to capture an image, a linear motion detector and a rotational motion detector. This system also includes a controller configured to receive a signal from the imaging device relating to capture of the image and to responsively cause the linear motion detector and the rotational motion detector to detect motion-related information. Finally, this particular system includes a motion calculator configured to recover camera motion associated with the image based upon the detected motion-related information and to infer imaging device motion induced blur of the image and an image deblurring component configured to reduce imaging device induced blur from the image utilizing the inferred camera motion induced blur.

Abstract: A computer implemented method tracks 3D positions of an object moving in a scene. A sequence of images is acquired of the scene with a set of cameras such that each time instant a set of images are acquired of the scene, in which each image includes pixels. Each set of images is aggregated into a synthetic aperture image including the pixels, and the pixels in each the set of images are matched corresponding to multiple locations and multiple depths of a target window with an appearance model to determine scores for the multiple locations and multiple depths. A particular location and a particular depth having a maximal score is selected as the 3D position of the moving object.

Abstract: A computer implemented method tracks 3D positions of an object moving in a scene. A sequence of images is acquired of the scene with a set of cameras such that each time instant a set of images are acquired of the scene, in which each image includes pixels. Each set of images is aggregated into a synthetic aperture image including the pixels, and the pixels in each the set of images are matched corresponding to multiple locations and multiple depths of a target window with an appearance model to determine scores for the multiple locations and multiple depths. A particular location and a particular depth having a maximal score is selected as the 3D position of the moving object.