A method for enhancing a set of object scan images may be provided. The set of object scan images comprises at least two scan images. The method comprises determining first distance and determining an interpolated scan image, by applying an interpolation algorithm comprising determining any pixel of the interpolated scan image as a white pixel if the corresponding pixels on the scan images are both white. The same applies to black pixels. The method may further comprise determining any pixel which corresponding pixels are black on one of the two image scans and white on the other image scans as white (black) if a predefined percentage of the directly surrounding pixels is white (black) in the interpolated scan image. Furthermore, the method comprises inserting the interpolated scan image between the first scan image and the second scan image and repeating the above steps until a stop condition is met.

A system for enhancing a set of object scan images may be provided. The set of object scan images may also here comprise at least a first scan image, located at a distance therefrom a second scan image.

A system and method for generating radiometrically corrected surface images is provided. This includes providing one or more surface images of a first resolution of a surface area in the form of digital images which has a surface image sensor measurement of an intensity value of radiation in a given wavelength band reflected from the surface for each pixel of the images. A reference image of a second resolution of a corresponding surface area is provided to the surface of the surface image. The reference image has a surface reflectance for each pixel of the reference image for an equivalent wavelength band to the given wavelength band of the surface image. A functional relationship is modelled which relates the surface image sensor measurement for pixels of a surface image to the surface reflectance for pixels of the reference image to provide an estimated surface reflectance for each pixel of the surface images.

Vasculature modeling systems and methods are disclosed that generate an enhanced 3D model based on a combination of two dimensional, 2D, imaging data of a region of interest and 3D imaging data of the region of interest. A hemodynamic simulation is performed using the enhanced 3D model to derive at least one hemodynamic parameter based on the hemodynamic simulation.

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.

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.

An image correction method includes: acquiring band images obtained by imaging a subject, and a high-resolution image having a resolution higher than that of the band images; acquiring a position difference between the object band image and the reference band image among the band images; by using a pixel of the object band image as an object pixel, for each object pixel, determining a pixel value of each sub-region obtained by dividing the imaging region of the object pixel into a plurality of regions, based on the pixel value of the object pixel and a relationship between pixel values of the pixels of the high-resolution image corresponding to the object pixel; and creating a corrected band image that holds a pixel value of light on the object band image at the pixel position of the reference band image, from the determined pixel value of each sub-region and the position difference.

In implementations of super-resolution with reference images, a super-resolution image is generated based on reference images. Reference images are not constrained to have same or similar content as a low-resolution image being super-resolved. Texture features indicating high-frequency content are extracted into texture feature maps, and patches of texture feature maps of reference images are determined based on texture feature similarity. A content feature map indicating low-frequency content of an image is adaptively fused with a swapped texture feature map including patches of reference images with a neural network based on similarity of texture features. A user interfaces allows a user to select regions of multiple reference images to use for super-resolution. Hence, a super-resolution image can be generated with rich texture details incorporated from multiple reference images, even in the absence of reference images having similar content to an image being upscaled.

High-speed hyperspectral (HSHS) video reconstruction is implemented in an imaging system. A hyperspectral snapshot is determined. A panchromatic video is determined corresponding to a frame time of the hyperspectral snapshot, the panchromatic video including a plurality of panchromatic frames. An HSHS video is generated by building a patch dictionary based at least in part on the panchromatic video and generating at least one frame of the HSHS video based at least in part on the patch dictionary, the panchromatic video, and the hyperspectral snapshot. A computing device can include a dictionary component configured to determine the patch dictionary and a reconstruction component configured to generate at least one HSHS frame based at least in part on the patch dictionary, the panchromatic video, and the hyperspectral snapshot.

There is provided an apparatus and a method that perform a process of improving the quality of a low-quality image such as a far-infrared image. The apparatus includes an image correction unit that repeatedly performs an image correction process using a plurality of processing units in at least two stages. The image correction unit inputs a low-quality image to be corrected and a high-quality image which is a reference image. Each of the processing units in each stage performs a correction process for the low-quality image, using a class correspondence correction coefficient corresponding to a feature amount extracted from a degraded image of the high-quality image. A processing unit in a previous stage performs the correction process, using a class correspondence correction coefficient corresponding to a feature amount extracted from an image which has a higher degradation level than that in a processing unit in a subsequent stage.