Raytheon Company

Dave S Douglas

Jingning Pan

Arthur M Newman

Vitaliy M Kaganovich

Michael J Mahnken

Nicholas Sun

Hong Jiang

Alexandra G Levinson

Jonathan A Cain

A method of deblurring an image taken by a moving imaging sensor. The method includes: receiving an image from the imaging sensor; collecting motion data indicative of motion of the sensor while capturing the image; applying successive approximations in deblurring based on certain features of the image; and applying a Maximum A Posteriori (MAP) deblurring process with adaptive priors to deblur the image and create a deblurred image, wherein the MAP deblurring process applies a first prior in certain regions and second prior in other regions.

Deblurring motion-blurred images using map estimate and adaptive priors

A system and method for capturing an unmanned aerial vehicle includes a net configured to receive the unmanned aerial vehicle, an infrared emitter arrangement including a plurality of infrared emitters arranged around the net, an infrared sensor mounted to the unmanned aerial vehicle and configured to detect the infrared emitter arrangement, and a processor that is in communication with the infrared sensor and configured to adjust an azimuth and elevation of the unmanned aerial vehicle based on the detected infrared emitter arrangement in a field-of-view of the infrared sensor.

System and method for net-capture of unmanned aerial vehicle

A method of de-smearing an image includes capturing image data from an imaging sensor and collecting motion data indicative of motion of the sensor while capturing the image data. The motion data is collected at a higher frequency than an exposure frequency at which the image data is captured. The method includes modeling motion of the sensor based on the motion data, wherein motion is modeled at the higher frequency than the exposure frequency. The method also includes modeling optical blur for the image data, modeling noise for the image data, and forming a de-smeared image as a function of the modeled motion, the modeled blur, and the modeled noise, and the image data captured from the imaging sensor.

Arbitrary motion smear modeling and removal

Embodiments of a target-tracking system and method of determining an initial target track in a high-clutter environment are generally described herein. The target-tracking system may register image information of first and second warped images with image information of a reference image. Pixels of the warped images may be offset based on the outputs of the registration to align each warped images with the reference image. A three-frame difference calculation may be performed on the offset images and the reference image to generate a three-frame difference output image. Clutter suppression may be performed on the three-frame difference image to generate a clutter-suppressed output image for use in target-track identification. The clutter suppression may include performing a gradient operation on a background image to remove any gradient objects.

Three-frame difference moving target acquisition system and method for target track identification

Restoration based super-resolution combines a video sequence of low-resolution noisy blurred images using maximum likelihood estimation without regularization to produce a higher resolution image or video sequence up to a maximum enhancement factor r for the given video sequence. The maximum r value for a given sequence of low-resolution images that guarantees each high-resolution bin (pixel) will have at least one assigned pixel value from a low-resolution image is calculated. With the knowledge of max r, the system can select an r less than or equal to max r and thus operate without regularization. System robustness is further enhanced by providing a modified MLE estimator that can perform super resolution for arbitrary real-valued r so that enhancement is not limited to integer values.

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Marina Del Ray, CA, United States of America

Dave S Douglas