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 system and method are provided for capturing an image with correct skin tone exposure. In use, one or more faces are detected having threshold skin tone within a scene. Next, based on the detected one or more faces, the scene is segmented into one or more face regions and one or more non-face regions. A model of the one or more faces is constructed based on a depth map and a texture map, the depth map including spatial data of the one or more faces, and the texture map includes surface characteristics of the one or more faces. The one or more images of the scene are captured based on the model. Further, in response to the capture, the one or more face regions are processed to generate a final image.

Methods and systems are provided for controlling an x-ray imaging system. In one embodiment, a method for an x-ray imaging system includes acquiring, with the x-ray imaging system, a first image as an x-ray tube current of the x-ray imaging system is ramping to a target x-ray tube current, determining a corrected brightness of the first image, the corrected brightness including a measured brightness of the first image corrected by a feedback x-ray tube current relative to the target x-ray tube current, and updating the target x-ray tube current based on the corrected brightness of the first image.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

A minimum luminance value of a display luminance information is larger than a minimum luminance value of a print luminance information. And in a conversion, a luminance value of a dark region of input image data is converted to a luminance value of a dark region of output image data such that a contrast of a dark region that includes the minimum luminance value of the print luminance information becomes closer to a contrast of a dark region that includes the minimum luminance value of the display luminance information.

A solution is proposed for assisting a medical procedure. A corresponding method comprises acquiring a luminescence image (205F), based on a luminescence light, and an auxiliary image (205R), based on an auxiliary light different from this luminescence light, of a field of view (103); the field of view (103) contains a region of interest comprising a target body of the medical procedure (containing a luminescence substance) and one or more foreign objects. An auxiliary informative region (210Ri) representative of the region of interest without the foreign objects is identified in the auxiliary image (205R) according to its content, and a luminescence informative region (210Fi) is identified in the luminescence image (205F) according to the auxiliary informative region (210Ri). The luminescence image (205F) is processed limited to the luminescence informative region (210Fi) for facilitating an identification of a representation of the target body therein. A computer program and a corresponding computer program product for implementing the method are also proposed. Moreover, a computing device for performing the method and an imaging system comprising it are proposed. A medical procedure based on the same solution is further proposed.

Computer-implemented image correction for a biomarker test includes a biomarker test which has a calibration array and a biomarker site, wherein the calibration array comprises plural colored patches and the biomarker site is color-responsive to indicate a measurement of biomarkers present. The method comprises: storing a reference color value for each of the plural colored patches; receiving an image of the biomarker test; defining shading of pixels of the image as D, a combination of a plurality of basis functions; defining a color correction matrix, M having parameters that when solved correct color of the calibration array in the image to the corresponding stored values; solving D and M for pixels of the image excluding pixels of the biomarker site; using D and M to interpolate values for pixels of the biomarker site of the image to generate a color and shading corrected image of the biomarker site.

Embodiments of the present application provide a medical image synthesis device and method. According to an embodiment, a method includes acquiring a first medical image and a second medical image and registering the first medical image with the second medical image. The method includes determining a first parameter value at each pixel location on the registered first medical image and a second parameter value at each pixel location on the second medical image. The method includes multiplying the first parameter value with the second parameter value at the same pixel location on the registered first medical image and the second medical image and generating synthetic image data based on the multiplication result.

An imaging system can include a first and second camera configured to capture first and second sets of oblique images along first and second scan paths, respectively, on an object area. A drive is coupled to a scanning mirror structure, having at least one mirror surface, and configured to rotate the structure about a scan axis based on a scan angle. The first and second cameras each have an optical axis set at an oblique angle to the scan axis and include a respective lens to focus first and second imaging beams reflected from the mirror surface to an image sensor located in each of the cameras. The first and second imaging beams captured by their respective cameras can vary according to the scan angle. Each of the image sensors captures respective sets of oblique images by sampling the imaging beams at first and second values of the scan angle.

Embodiments are disclosed for generating multiple color theme variations from an input image using learned color distributions. A method of generating multiple color theme variations from an input image using learned color distributions includes obtaining, by a user interface manager, an input image, determining, by a color extraction manager, one or more color priors based on the input image, generating, by a color distribution modeling network, a plurality of color theme variations based on the one or more color priors, ranking, by a color theme evaluation network, the plurality of color theme variations, and generating, by a recolor manager, a plurality of recolored output images using the plurality of color theme variations.