The present invention discloses an intelligent cloud-based photo enhancement system to improve the quality and aesthetics of an image captured via an electronic device. The cloud-based photo enhancement system comprises a cloud database containing a collection of high-quality photos taken at various locations worldwide and under different environment settings. Information from photos stored in the cloud database taken using better photographic capability cameras is used to improve details that the new photo fails to capture. The system relies not only on machine learning models but also utilizes data associated with the new image collected from the hardware of the electronic device such as camera settings, GPS and phone sensors, which results in more reliable information.

An imaging device for producing images of a scene, the imaging device comprising: a first and a second hyperchromatic lens being arranged in a stereoscopic configuration to receive light from the scene; image sensor circuitry configured to capture a first and second image of the light encountered by the first and the second lens respectively; processor circuitry configured to: produce depth information using the captured first and second images of the scene and produce a resultant first and second image of the scene using both the captured first and second image and the depth information.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

There is provided a medical image processing apparatus including: an association processing section configured to associate multiple medical captured images in which an observation target is imaged by each of multiple imaging devices including imaging devices in which one or both of an in-focus position and an in-focus range are different; and a compositing processing section configured to depth-composite each of a medical captured image for a right eye and a medical captured image for a left eye among the multiple medical captured images by using an associated other medical captured image.

A computer-implemented method for processing electronic medical images, the method including receiving a plurality of electronic medical images of a medical specimen. Each of the plurality of electronic medical images may be divided into a plurality of tiles. A plurality of sets of matching tiles may be determined, the tiles within each set corresponding to a given region of a plurality of regions of the medical specimen. For each tile of the plurality of sets of matching tiles, a blur score may be determined corresponding to a level of image blur of the tile. For each set of matching tiles, a tile may be determined with the blur score indicating the lowest level of blur. A composite electronic medical image, comprising a plurality of tiles from each set of matching tiles with the blur score indicating the lowest level of blur, may be determined and provided for display.

A system uses range and Doppler velocity measurements from a lidar system and images from a video system to estimate a six degree-of-freedom trajectory (6DOF) of a target. The 6DOF transformation parameters are used to transform multiple images to the frame time of a selected image, thus obtaining multiple images at the same frame time. These multiple images may be used to increase a resolution of the image at each frame time, obtaining the collection of the superresolution images.

The present disclosure provides systems, apparatus, methods, and computer-readable media that support multi-frame depth-of-field (MF-DOF) for deblurring background regions of interest (ROIs), such as background faces, that may be blurred due to a large aperture size or other characteristics of the camera used to capture the image frame. The processing may include the use of two image frames obtained at two different focus points corresponding to the multiple ROIs in the image frame. The corrected image frame may be determined by deblurring one or more ROIs of the first image frame using an AI-based model and/or local gradient information. The MF-DOF may allow selectively increasing a depth-of-field (DOF) of an image to provide focused capture of multiple regions of interest, without causing a reduction in aperture (and subsequently an amount of light available for photography) or background blur that may be desired for photography.

Systems and methods for generating and processing images captured while inspecting above-ground pipelines are disclosed. Embodiments may include a robotic crawler or other devices which carry imaging equipment and traverse a target pipe which are configured to capture image data simultaneously from a plurality of angles. Such systems may substantially reduce and in some cases overcome the need to take multiple traversals of a pipeline under inspection. Embodiments may also be directed toward control systems for such devices as well as image processing systems which process the multiple image sets to produce a composite imaging result.

An image fusion method and apparatus and a training method and apparatus for an image fusion model are provided, which relate to the field of artificial intelligence, and specifically, to the field of computer vision. The image fusion method includes: obtaining a to-be-processed color image, an infrared image, and a background reference image, where the infrared image and the to-be-processed color image are shot for a same scene; and inputting the to-be-processed color image, the infrared image, and the background reference image into an image fusion model for feature extraction, and performing image fusion based on extracted features to obtain a fused image. This method can improve image quality of a fused image, and also ensure accurate and natural color of the fused image.

Embodiments of this application provide an image reconstruction method and device. The method includes: inputting a first image into a newly constructed super-resolution model to obtain a reconstructed second image, where a resolution of the second image is higher than that of the first image. The newly constructed super-resolution model is obtained by training an initial super-resolution model by using an error loss. The error loss includes a pixel mean square error and an image feature mean square error. The image feature in the image feature mean square error includes at least one of a texture feature, a shape feature, a spatial relationship feature, and an image high-level semantic feature. According to the embodiments of this application, the quality of a reconstructed image can be improved.