Systems and methods for image processing for increasing resolution of a multi-spectral image. Accept a multi-spectral image including a set of images of a scene. A memory to store a set of dictionaries trained for different channels, and a set of filters trained for the different channels. A hardware processor is to process the set of images of the different channels with the set of filters, and to fuse, for each channel, the set of structures, to produce a set of fused structures. Wherein a fused structure of the channel is fused as a weighted combination of the set of structures using weights corresponding to the channel, such that the fused structures of different channels are combined with different weights. To process the set of fused structures with corresponding dictionaries from the set of dictionaries, to produce a super-resolution multi-spectral image. An output interface to render the super-resolution multi-spectral image.

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.

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.

Multiband radar fusion is provided. The method comprises collecting first radar data from a first radar having a first frequency and collecting second radar data from a second radar having a second frequency different from the first frequency. A hybrid, dual-domain complex value convolutional neural network (CV-CNN), fuses the first and second radar data. The CV-CNN alternates between operating in the wavenumber-domain and the spatial-domain of the first and second radar data. A synthetic aperture radar image is reconstructed from the fused first and second radar data according to Fourier-based algorithm.

Systems and methods for image processing for increasing resolution of a multi-spectral image. Accept a multi-spectral image including a set of images of a scene. A memory to store a set of dictionaries trained for different channels, and a set of filters trained for the different channels. A hardware processor is to process the set of images of the different channels with the set of filters, and to fuse, for each channel, the set of structures, to produce a set of fused structures. Wherein a fused structure of the channel is fused as a weighted combination of the set of structures using weights corresponding to the channel, such that the fused structures of different channels are combined with different weights. To process the set of fused structures with corresponding dictionaries from the set of dictionaries, to produce a super-resolution multi-spectral image. An output interface to render the super-resolution multi-spectral image.

A system and methods are described for generating a super-resolution depth-map. A method includes: determining a plurality of unmeasured depth-map positions using measured depth-elements from a first sensor and spatial-elements from a second sensor; for each of the plurality, calculating estimated depth-elements using a gradient-based optimization; and generating a super-resolution depth-map that comprises the measured and estimated depth-elements.

An information processing device according to the present invention includes a memory; and at least one processor coupled to the memory. The processor performing operations. The operations includes: receiving first multiple-images; and generating, based on a first image in the first multiple-images, a third image relating to a second image in a second wavelength band different from a first wavelength band of the first image.

An image magnifying apparatus includes a processor configured to execute non-transitory machine readable instructions to configure the processor to, receive the image data, generate a first interpolation pixel between pixels of the image data, by applying a first interpolation method based on a high-band spectrum of the image data, generate a second interpolation pixel between pixels of the image data, by applying a second interpolation method not based on the high-band spectrum of the image data, identify a pattern of pixels of the image data by extracting peripheral pixels of an interpolation object position in the image data, select whether to apply the first interpolation method to the interpolation object position or whether to apply the second interpolation method to the interpolation object position, and output one of the first interpolation pixel and the second interpolation pixel, as an output interpolation pixel, based on the selection.

Disclosed is an extraction method for time-space-spectrum four-dimensional remote sensing data. The method includes: obtaining remote sensing images at a preset coverage area during a preset time period (S1); re-projecting the remote sensing images in a way that the respective pixel positions of the respective remote sensing images at different time points are overlapped (S2); storing all of re-projected remote sensing images into a storage unit according to a first preset storage method, or storing all of the re-projected remote sensing images into the storage unit according to a second preset storage method (S3); determining, on the basis of requirement information for data extraction, whether current data format of remote sensing data is a data format matching the requirement information (S4); if yes, determining, on the basis of the requirement information, a storage location of required remote sensing data, and extracting the required remote sensing data (S5). In the case storing is executed through the two preset storage methods above, the time consumption for locating the storage location can be reduced, thus the efficiency in processing the four-dimensional remote sensing data can be improved.

Provided is a device and a method for executing image quality improvement processing of an infrared light image. Included are: a feature amount calculating unit for receiving an infrared light image and a visible light image and extracting a feature amount from at least one of the images; and an image correcting unit for executing pixel value correction processing on the infrared light image on the basis of a reference area and a correction parameter determined depending on the feature amount. Further included are: a tap selection unit for determining the reference area used for the pixel value correction on the basis of the feature amount; and a correction parameter calculating unit for determining the correction parameter used for the pixel value correction on the basis of the feature amount. The image correcting unit executes the pixel value correction processing in which a tap determined by the tap selection unit and the correction parameter determined by the correction parameter calculating unit are applied.