A method and breast imaging system for processing breast tissue image data includes feeding image data of breast images to an image processor, identifying image portions depicting breast tissue and high density elements and executing different processing methods on input images. A first image processing method involves breast tissue enhancement and high density element suppression, whereas the second image processing method involves enhancing high density elements. Respective three-dimensional sets of image slices may be generated by respective image processing methods, and respective two-dimensional synthesized images are generated and combined to form a two-dimensional composite synthesized image which is presented through a display of the breast imaging system. First and second image processing may be executed on generated three-dimensional image sets or two-dimensional projection images acquired by an image acquisition component at respective angles relative to the patient's breast.

Systems and methods for performing motion artifact correction in medical images. One method includes receiving, with an electronic processor, a medical image associated with a patient, the medical image including at least one motion artifact. The method also includes applying, with the electronic processor, a model developed using machine learning to the medical image for correcting motion artifacts, the model including at least one of a spatial transformer network and an attention mechanism network. The method also includes generating, with the electronic processor, a new version of the medical image, where the new version of the medical image at least partially corrects the at least one motion artifact.

Systems and methods for performing motion artifact correction in medical images. One method includes receiving, with an electronic processor, a medical image associated with a patient, the medical image including at least one motion artifact. The method also includes applying, with the electronic processor, a model developed using machine learning to the medical image for correcting motion artifacts, the model including at least one of a spatial transformer network and an attention mechanism network. The method also includes generating, with the electronic processor, a new version of the medical image, where the new version of the medical image at least partially corrects the at least one motion artifact.

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.

A background image generator (21) stores, in a storage device (3), a skeleton image obtained by calculating a feature quantity for each of multiple first thermal images (Din1) obtained by imaging by a thermal image sensor (1) in the same field of view or multiple sorted images (Dc) generated from the first thermal images, generating an average image from the first thermal images or sorted images, sharpening the average image, and then extracting a skeleton component. An image corrector (22) corrects, by using the skeleton image stored in the storage device (3), a second thermal image (Din2) obtained by imaging by the thermal image sensor in the same field of view as the first thermal images, thereby generating a corrected thermal image. It is possible to generate a sharp thermal image with a high S/N ratio.

In one embodiment, a computing system may determine a focus point of a viewer based on received sensor data. The system may determine, for a current frame, a first viewing region encompassing a focus point of the viewer and a second view region excluding the first viewing region. The system may determine, for the current frame, color values for the first viewing region using respective first sampling resolutions, and color values for the second viewing region using respective second sampling resolutions. At least one second sampling resolution may be lower than a corresponding first sampling resolution associated with a same color channel. At least two of the second sampling resolutions for the color channels of the second viewing region may be different from each other. The system may output the color values for the first viewing region and the second viewing region of the current frame for display.

An objective of an endoscope can be evaluated by collecting a series of differently focused images and digitally stitching them together to obtain a final image for the endoscope that can be then evaluated. Movable optics and/or a camera can be used to collect the series of differently focused images. Image processing algorithms can be used to evaluate the collected images in terms of image sharpness and identify the areas at which each image is in relatively good focus. Once the areas of good focus are identified, the image processing algorithms can extract the areas of good focus. The digital stitching algorithms can be used to assemble the extracted areas of good focus to form a final image where most of the target scene should be in focus. The final image is then reviewed to determine the acceptability of the objective.

Described herein is a method, and system for training a deblurring model and deblurring an image (e.g., SEM image) of a patterned substrate using the deblurring model and depth data associated with multiple layers of the patterned substrate. The method includes obtaining, via a simulator using a target pattern as input, a simulated image of the substrate, the target pattern comprising a first target feature to be formed on a first layer, and a second target feature to be formed on a second layer located below the first layer; determining, based on depth data associated with multiple layers of the substrate, edge range data for features of the substrate; and adjusting, using the simulated image and the edge range data associated with the target pattern as training data, parameters of a base model to generate the deblurring model to a deblur image of a captured image.

A system that can reduce the cost for correcting artifacts due to motion in the reconstruction of CT images includes acquiring the temporarily corrected projection image and determining the reference center position correction function and the parameter of the temporary reference center position correction function by calculating a degree of coincidence between the temporarily corrected projection image and the projection image at the imaging angle opposing thereto, and correcting the main imaging data or the projection image based on the main imaging data using the reference center position correction function and the relative motion correction function.

In an embodiment of the present inventive concept, there is provided an 0 smoothing method performed on the basis of deep gradient prior information to improve sharpness of an image by an image quality improving device, and the method comprises: a gradient-improved image generation step of generating a gradient-improved image by minimizing the gradients of pixels of an original image, by the image quality improving device; and a smoothing-improved image generation step of generating a smoothing-improved image smoothing-processed through one-step (.sub.0) estimation on the gradient-improved image, by the image quality improving device.