Abstract:
Methods and systems for generating a depth map are provided. The method includes projecting an infrared (IR) dot pattern onto a scene. The method also includes capturing stereo images from each of two or more synchronized IR cameras, detecting a number of dots within the stereo images, computing a number of feature descriptors for the dots in the stereo images, and computing a disparity map between the stereo images. The method further includes generating a depth map for the scene using the disparity map.
Abstract:
A “Blur Remover” provides various techniques for constructing deblurred images from a sequence of motion-blurred images such as a video sequence of a scene. Significantly, this deblurring is accomplished without requiring specialized side information or camera setups. In fact, the Blur Remover receives sequential images, such as a typical video stream captured using conventional digital video capture devices, and directly processes those images to generate or construct deblurred images for use in a variety of applications. No other input beyond the video stream is required for a variety of the embodiments enabled by the Blur Remover. More specifically, the Blur Remover uses joint global motion estimation and multi-frame deblurring with optional automatic video “duty cycle” estimation to construct deblurred images from video sequences for use in a variety of applications. Further, the automatically estimated video duty cycle is also separately usable in a variety of applications.
Abstract:
Tilt is reduced or eliminated in captured digital images. Edges in a first image are detected. Angles corresponding to the detected edges are determined. A dominant angle is selected from the determined angles. The first image is rotated according to the selected dominant angle to generate a second image. The second image is a de-tilted version of the first image.
Abstract:
A low light noise reduction mechanism may perform denoising prior to demosaicing, and may also use parameters determined during the denoising operation for performing demosaicing. The denoising operation may attempt to find several patches of an image that are similar to a first patch, and use a weighted average based on similarity to determine an appropriate value for denoising a raw digital image. The same weighted average and similar patches may be used for demosaicing the same image after the denoising operation.
Abstract:
A system is described for reducing artifacts produced by a rolling shutter capture technique in the presence of high-frequency motion, e.g., produced by large accelerations or jitter. The system operates by computing low-frequency information based on the motion of points from one frame to the next. The system then uses the low-frequency information to infer the high-frequency motion, e.g., by treating the low-frequency information as known integrals of the unknown underlying high-frequency information. The system then uses the high-frequency information to reduce the presence of artifacts. In effect, the correction aims to re-render video information as though all the pixels in each frame were imaged at the same time using a global shutter technique. An auto-calibration module can estimate the value of a capture parameter, which relates to a time interval between the capture of two subsequent rows of video information.
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 process for compressing and decompressing non-keyframes in sequential sets of contemporaneous video frames making up multiple video streams where the video frames in a set depict substantially the same scene from different viewpoints. Each set of contemporaneous video frames has a plurality frames designated as keyframes with the remaining being non-keyframes. In one embodiment, the non-keyframes are compressed using a multi-directional spatial prediction technique. In another embodiment, the non-keyframes of each set of contemporaneous video frames are compressed using a combined chaining and spatial prediction compression technique. The spatial prediction compression technique employed can be a single direction technique where just one reference frame, and so one chain, is used to predict each non-keyframe, or it can be a multi-directional technique where two or more reference frames, and so chains, are used to predict each non-keyframe.
Abstract:
A system is described for reducing artifacts produced by a rolling shutter capture technique in the presence of high-frequency motion, e.g., produced by large accelerations or jitter. The system operates by computing low-frequency information based on the motion of points from one frame to the next. The system then uses the low-frequency information to infer the high-frequency motion, e.g., by treating the low-frequency information as known integrals of the unknown underlying high-frequency information. The system then uses the high-frequency information to reduce the presence of artifacts. In effect, the correction aims to re-render video information as though all the pixels in each frame were imaged at the same time using a global shutter technique. An auto-calibration module can estimate the value of a capture parameter, which relates to a time interval between the capture of two subsequent rows of video information.
Abstract:
An image sharpening technique with halo suppression is presented. Generally, one implementation of this technique completely suppresses the haloing effect typically caused by image sharpening by restricting values to within the local minimum and maximum intensities of the unsharpened image. Thus, if the sharpened value is below the local minimum, it is replaced with the local minimum. Similarly, the local maximum is taken if the sharpened value exceeds it. In other implementations of the technique, haloing caused by image sharpening is suppressed but not completely eliminated, thereby producing a subtle haloing effect.
Abstract:
A chromatic aberration (CA) correction technique is presented that substantially removes CA from an image captured by a digital camera. In general, the effects of any in-camera sharpening are reversed by applying a blurring kernel. The image is then super-sampled to approximate its state prior to the application of in-camera sampling. One of the color channels is designated as a reference channel, and an objective function is established for each of the non-reference channels. The reference color channel is assumed to be CA-free, while the objective functions are used to compute the unknown CA parameters for each non-reference channel. These sets are used in a CA removal function to substantially remove the CA associated with each of the non-reference channels. The image is then sampled to return it to its original resolution, and a sharpening filter is applied if needed to undo the effects of the previously applied blurring kernel.