Systems and methods for detecting aortic aneurysms using ensemble based deep learning techniques that utilize numerous computed tomography (CT) scans collected from numerous de-identified patients in a database. The system includes software that automates the analysis of a series of CT scans as input (in DICOM file format) and provides output in two dimensions: (1) ranking CT scans by risks of adverse events from aortic aneurysm, (2) providing aortic aneurysm size estimates. A repository of CT scans may be used for training of deep neural networks and additional data may be drawn from localized patient information from institutions and hospitals which grant permission.

A training data generation device includes a computer, and a computer-readable storage medium. The computer is configured to: receive an input of an annotation for second image data obtained by imaging an observation target; reflect a result of the annotation in first image data that is related to the same observation target as the observation target of the second image data, the first image data having a different at least one of imaging mode and display mode from the second image data; and generate training data for creating an inference model by using the first image data and the result of the annotation reflected in the first image data, the first image data including image data of a plurality of images, and the second image data being image data of an image obtained by combining the plurality of images included in the first image data.

An image processor receives first image data representing an image. The first image data comprising a plurality of color values corresponding to a plurality of pixels in the image. The image processor determines, using a trained machine learning model, second image data based on the first image data. The second image data comprises surface spectral reflection values corresponding to the plurality of pixels in the image, where the surface spectral reflection values are distributed across a plurality of wavelengths of visible light in the image. The image processor then performs at least one image processing operation with respect to the image using the second image data.

Systems and techniques are described for image processing. An imaging system can include an image sensor that captures image data. An image signal processor (ISP) of the imaging system can demosaic the image data. The imaging system can input the image data into one or more trained machine learning models, in some cases along with metadata associated with the image data. The one or more trained machine learning models can output settings for a set of parameters of the ISP based on the image data and/or the metadata. The imaging system can generate an output image by processing the image data using the ISP, with the parameters of the ISP set according to the settings. Each pixel of the pixels of the image data can be processed using a respective setting for adjusting a corresponding parameter. The parameters of the ISP can include gain, offset, gamma, and Gaussian filtering.

An exemplary method of monitoring a bath process includes processing a first wafer by submerging the first wafer within a bath solution; capturing a video of the bath solution containing the first wafer during a first time interval; analyzing the video based on intensity of light captured in a frame of the video; and based on analyzing the video, determining a first metric of the bath solution during the first time interval.

An image processing method, an electronic device and a storage medium. The method includes: under a preset condition, when detecting that an image currently captured by a camera module contains a human face, determining a reference photosensitivity corresponding to each frame of images to be captured according to a current jitter degree of the camera module; determining an exposure duration corresponding to each frame of images to be captured according to luminance of a current shooting scene, the reference photosensitivity corresponding to each frame of the images to be captured, and a preset mode of exposure compensation; capturing a plurality of frames of images in sequence according to the reference photosensitivity and the exposure duration corresponding to each frame of the images to be captured; and performing synthesis processing on the captured plurality of frames of images to generate a target image.

A method for enhancing media includes: receiving, by an electronic device, a media stream; performing, by the electronic device, an alignment of a plurality of frames of the media stream; correcting, by the electronic device, a brightness of the plurality of frames; selecting, by the electronic device, one of a first neural network, a second neural network, or a third neural network, by analyzing parameters of the plurality of frames having the corrected brightness, wherein the parameters include at least one of shot boundary detection and artificial light flickering; and generating, by the electronic device, an output media stream by processing the plurality of frames of the media stream using the selected one of the first neural network, the second neural network, or the third neural network.

An image processing device includes a processor; and a memory to store the program which performs processing including: measuring a first texture amount indicating luminance variation in image regions, based on the image regions obtained by dividing the captured image and previously determining an image processing target region based on the first texture amount; judging whether the image processing on a current captured image is necessary based on luminance of the image processing target region in the current captured image; calculating a second texture amount indicating luminance variation in the image processing target region, and judging whether the image processing should be performed on the image processing target region or not based on the second texture amount; and performing the image processing on the image processing target region on which the judging based on the second texture amount is that the image processing should be performed.

A dynamic image enhancement method and device using backlight adjustment, and a computer apparatus. The method comprises: acquiring coding information carried by display content; acquiring an optical indicator parameter of a display; parsing the display content according to the coding information and the optical indicator parameter, and acquiring information of current display content; performing analysis and computation according to the information of the display content, and acquiring analysis data; acquiring a first detail statistics weight and a second detail statistics weight according to the analysis data; acquiring a backlight control parameter and a signal control curve according to the first detail statistics weight and the second detail statistics weight; and adjusting the detail of a current image according to the backlight control parameter and the signal control curve.

The present disclosure provides methods, apparatuses, and computer-readable mediums for image processing. In some embodiments, a method of image processing includes acquiring, from a user, a first image. The method further includes removing, using an image de-filter network, a filter effect applied to the first image to generate a second image. The method further includes obtaining, based on the first image and the second image, an image filter corresponding to the filter effect. The method further includes rendering a third image using the obtained image filter to output a fourth image.