A device includes 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. The device further includes 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.

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).

Systems, methods and apparatus for image processing for reconstructing a super resolution (SR) image from multispectral (MS) images. A processor to iteratively, fuse a MS image with an associated PAN image of the scene. Each iteration includes using a gradient descent (GD) approach with a learned forward operator, to generate an intermediate high-resolution multispectral (IHRMS) image with an increased spatial resolution and a smaller error to the DSRMS image compared to the stored MS image. Project the IHRMS image using a trained convolutional neural network (CNN) to obtain an estimated synthesized high-resolution multispectral (ESHRMS) image, for a first iteration. Use the ESHRMS image and the PAN image, as an input to the GD approach for following iterations. The updated IHRMS image is an input to another trained CNN for the following iterations. After predetermined number of iterations, output the fused high-spatial and high-spectral resolution MS image.

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.

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.

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).

Super resolution and color motion artifact correction in a pulsed hyperspectral, fluorescence, and laser mapping imaging system. A method includes actuating an emitter to emit pulses of electromagnetic radiation and sensing reflected electromagnetic radiation with a pixel array of an image sensor. The method includes detecting motion across two or more sequential exposure frames, compensating for the detected motion, and combining the two or more sequential exposure frames to generate an image frame. The method is such that at least a portion of the pulses of electromagnetic radiation emitted by the emitter comprises one or more of: electromagnetic radiation having a wavelength from about 513 nm to about 545 nm, from about 565 nm to about 585 nm, from about 900 nm to about 1000 nm, an excitation wavelength of electromagnetic radiation that causes a reagent to fluoresce, or a laser mapping pattern.

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.

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 system and method for generating high-resolution stereo images and depth map in multi-camera systems having multiple cameras with different resolutions and view angles. One method is to improve the lower resolution image and combining it with the higher resolution image, then the resulting image is processed by extensive algorithms to ensure utmost high quality. The system can also handle non-planar image contents. The process is to generate a crude depth map first and then divide the map into multiple layers. Each layer will be separately registered. The results from the registered layers will be merged to improve the depth map generation. The improved depth map could be repeatedly fed back to the beginning of the process to further improve the registration performance. The system and method can generate stereo images using uncalibrated cameras with different views and resolutions.