Disclosed is a method for acquiring and forming a spectrometry image, including the following steps: a) acquiring an initial structural image of an area of a sample; b) breaking down the initial structural image so as to determine a multi-scale spatial sample of the area of the sample; c) determining a plurality of spectrometry measurement positions in the area of the sample, as a function of the multi-scale spatial sampling determined in step b); d) consecutively, for each spectrometry measurement position determined in step c), positioning the excitation beam and acquiring a spectrometry measurement; and e) reconstructing a spectrometry image point-by-point from the spectrometry measurements acquired in step d).

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.

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.

An image processing method using a first image sensor covering the visible range and a second sensor covering the infra-red range. The method includes a) acquiring a first image I.sub.1 of said given zone of space by means of the first image sensor, b) acquiring a second image I.sub.2 of said given zone of space by means of the second image sensor, c) performing a decomposition of the first image I.sub.1 so as to obtain at least one luminance image I.sub.1L, d) obtaining an image I.sub.f resulting from digital fusion of the luminance image I.sub.1L and of the second image I.sub.2, e) adding colour information to the fusion image I.sub.f or during the fusion stage d).

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.

According to one or more aspects of the disclosed subject matter, reconstructing 3D radio frequency tomographic images can include detecting a plurality of frequencies of a target and generating a low resolution image for each frequency. Additionally, a high frequency low resolution image can be selected from the plurality of low resolution images to be a reference image. Further, pixel values of the reference image can be obtained and the pixel values of the reference image can be compared to each of the plurality of low resolution images. The method can also include determining new features in each of the low resolution images based on the comparisons, generating a high resolution grid based on the new features determined by the comparisons, wherein the high resolution grid is generated using interpolation, performing deconvolution of the high resolution grid using a Wiener filter, and outputting a high resolution image.

The present invention presents four fusion approaches, which can be directly applied to Worldview-3 (WV-3) images. Moreover, they can also be applied to other current or future satellite images that have similar characteristics of WV-3. The present invention also presents data processing methods, including image fusion method, anomaly detection method, material classification method, and concentration estimation method that utilize the high-resolution images generated by the mentioned fusion methods. There are four fusion approaches disclosed in the present invention, e.g. Parallel one-step fusion approach; Sequential fusion of various bands; Sequential-Parallel fusion; and Parallel-Sequential fusion.

A hybrid CT dataset is obtained from a combination of an integrating detector and a photon-counting detector. The hybrid CT dataset contains sparse spectral energy data and dense energy integration data. The dense panchromatic data sets inherit the resolution properties of the integrating detector while the sparse spectral data sets inherit the spectral information of the photon-counting detector. Subsequently, the sparse spectral energy data sets are pansharpened based upon at least one dense panchromatic data set that lacks spectral information according to a pansharpening algorithm.

The present disclosure provides a method and an apparatus for stretching an image. The method for stretching an image includes: selecting a corresponding stretching mode according to a mode selection parameter; generating a corresponding stretching filter group according to a stretching parameter and the selected stretching mode, and segmenting input image data into blocks; and processing the input image data segmented into blocks by the stretching filter group, to obtain stretched image data.