Techniques for generating a high resolution full color output image from lower resolution sparse color input images are disclosed. A camera generates images. The camera's sensor has a sparse Bayer pattern. While the camera is generating the images, IMU data for each image is acquired. The IMU data indicates a corresponding pose the camera was in while the camera generated each image. The images and IMU data are fed into a motion model, which performs temporal filtering on the images and uses the IMU data to generate a red-only image, a green-only image, a blue-only image, and a monochrome image. The color images are up-sampled to match the resolution of the monochrome image. A high resolution output color image is generated by combining the up-sampled images and the monochrome image.

A gated truncated readout system for position sensitive or imaging detectors that improves resolution over traditional readout systems. The readout system includes two or more amplifiers that receive a multichannel output analog data from the detector. Analog gates control circuitry, included in the readout circuit, receives the signals from the amplifiers, determines a fractional value of the sum-integral of the signals, and enables analog gates operation around an area of interest, disabling all other channels where noise dominates the signal value and thereby improving interpolation accuracy of the signals centroid position and the detector resolution. Filtered signals are transmitted to a centroid interpolation signal processing device for computation of the centroid position. As a result disabling all channels where noise dominates the signal value, the gated truncated readout system provides better accuracy improved detector resolution.

A display apparatus is provided. The display apparatus includes an input interface, a first storage, a display, and a processor. Pixel values corresponding to a predetermined number of lines in an image input through the input interface are stored in the first storage. The processor acquires a first patch of a predetermined size by sampling a number of pixel values located in an outer region of a matrix centering about a specific pixel value from among the pixel values stored in the first storage, acquires a high-frequency component for the specific pixel value based on the acquired first patch, and processes the input image based on the high-frequency component. The display displays the processed image.

A hyperspectral stereoscopic CubeSat with computer vision and artificial intelligence capabilities consists of a device and a data processing methodology. The device comprises a number of VIS-NIR-TIR hyperspectral sensors, a central processor with memory, a supervisor system running independently of the imager system, radios, a solar panel and battery system, and an active attitude control system. The device is launched into low earth orbit to capture, process, and transmit stereoscopic hyperspectral imagery in the visible and infrared portions of the electromagnetic spectrum. The processing methodology therein comprises computer vision and convolutional neural network algorithms to perform spectral feature identification and data transformations.

Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Systems, methods and apparatus for image processing for reconstructing a super resolution image from multispectral (MS) images. Receive image data and initialize a fused image using a panchromatic (PAN) image, and estimate a blur kernel between the PAN image and the MS images as an initialization function. Iteratively, fuse a MS image with an associated PAN image of a scene using a fusing algorithm. Each iteration includes: update the blur kernel based on a Second-Order Total Generalized Variation function to regularize a kernel shape; fuse the PAN image and MS images with the updated blur kernel based on a local Laplacian prior function to regularize the high-resolution information to obtain an estimated fused image; compute a relative error between the estimated fused image of the current iteration and a previous estimated fused image from a previous iteration, to a predetermined threshold, to stop iterations stop, to obtain a PAN-sharpened image.

A system and method for generating soil moisture data from satellite images of a geographical area using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a soil moisture data generator server 106. The method includes, (i) receiving, by a soil moisture data generator server, satellite images of the geographical area, (ii) pre-processing first set of satellite images, second set of satellite images, and third set of satellite images, (iii) interpolating, using spline interpolation, pre-processed first set of images, pre-processed second set of images, and pre-processed third set of images to generate high-resolution set of images, (iv) generating hydrological parameters from the high-resolution set of images, (v) training, a deep learning model, by providing historical hydrological parameters and historical soil moisture data associated with historical satellite images as training data to generate trained deep learning model, (v) generating soil moisture data on daily basis.

Techniques for generating a high resolution full color output image from lower resolution sparse color input images are disclosed. A camera generates images. The camera's sensor has a sparse Bayer pattern. While the camera is generating the images, IMU data for each image is acquired. The IMU data indicates a corresponding pose the camera was in while the camera generated each image. The images and IMU data are fed into a motion model, which performs temporal filtering on the images and uses the IMU data to generate a red-only image, a green-only image, a blue-only image, and a monochrome image. The color images are up-sampled to match the resolution of the monochrome image. A high resolution output color image is generated by combining the up-sampled images and the monochrome image.

Disclosed herein are a method and apparatus for fusing a panchromatic image and an infrared image. The apparatus includes: an image acquisition unit configured to acquire a panchromatic image and an infrared image having the same coordinate system; a filtering unit configured to generate a low-frequency panchromatic image by performing low-frequency filtering on the panchromatic image, and to generate a high-frequency panchromatic image by subtracting the low-frequency panchromatic image from the panchromatic image; an image correction unit configured to generate a corrected high-frequency panchromatic image, to construct a linear regression equation, and to generate a corrected infrared image by using the constructed linear regression equation; a scaling factor determination unit configured to determine the ratio at which the panchromatic image and the infrared image are fused together; and a fused image generation unit configured to generate a fused image through multiplication and addition.

Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.