There is provided a computer-implemented method for anti-aliasing an image frame in an image stream. The method comprises providing an input to a data model, the data model being configured to perform anti-aliasing on a present frame which is displayed after a previous frame in the image stream. The input comprises: an anti-aliased image of the previous frame; an aliased image of the present frame; an ID map of the previous frame; an ID map of the present frame; and a velocity map based on changes between the previous frame and the present frame. The data model processes the received inputs to correlate image portions of the previous frame and image portions of the present frame based on the inputs and performing anti-aliasing on the present frame based at least in part on the correlated image portions. An anti-aliased image of the present frame is received as an output of the data model.

A method includes downsampling an input frame and performing global contrast enhancement of the input frame based on the downsampled input frame to generate a globally contrast-enhanced frame. The method also includes generating texture maps at different scale resolutions based on the downsampled input frame. The method further includes generating clipped histograms at the different scale resolutions based on the downsampled input frame. The method also includes generating lookup tables (LUTs) at the different scale resolutions based on the texture maps and the clipped histograms. The method further includes applying the LUTs at the different scale resolutions to the input frame in order to generate locally contrast-enhanced frames. In addition, the method includes blending the locally contrast-enhanced frames and the globally contrast-enhanced frame to generate an output frame.

A visibility color recommendation system includes functionality operable to receive a user input defining a glyph to be displayed along with a digital image in a user interface. A color recommendation is generated, automatically and without user intervention, based on visibility of the glyph with respect to one or more colors of the digital image. The glyph is presented in the user interface as having a color from the digital image based on the color recommendation.

In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map indicating a possibility of lesion of the lesion candidate in the endoscopic video by a color. The display control means displays a position of a lesion on a frame of the endoscopic video based on the lesion candidate and the heat map.

A design revision apparatus includes a hardware processor that: applies, to an input image, attentional property evaluation that evaluates a portion easily attracting visual attention; performs impression evaluation that estimates an impression given by the input image to an observer; and combines a result of the attentional property evaluation with a result of the impression evaluation and presents a design revision proposal about an attentional property or an impression of the input image.

Embodiments of the present disclosure provide a video processing method, an apparatus and a device. The method includes: receiving a video generating request; acquiring a video template according to the video generating request, where the video template includes a plurality of placeholders, where each of the placeholders is configured to indicate at least one of a text type, an image type and a video type; acquiring a plurality of materials according to the video generating request, where types of the plurality of the materials include at least one of a text type, an image type and a video type; based on the types of the materials, importing the plurality of the materials into positions of corresponding placeholders in the video template respectively and performing rendering to obtain a synthesized video.

According to one embodiment, a medical image diagnostic apparatus includes a storage memory, processing circuitry, and a display. The storage memory stores data of a plurality of FFR distribution maps constituting a time series regarding a coronary artery, and data of a plurality of morphological images corresponding to the time series. The processing circuitry converts the plurality of FFR distribution maps into a plurality of corresponding color maps, respectively. The display displays a plurality of superposed images obtained by superposing the plurality of color maps and the plurality of morphological images respectively corresponding in phase to the plurality of color maps. The display restricts display targets for the plurality of color maps based on the plurality of FFR distribution maps or the plurality of morphological images.

Provided are a color correction matrix optimization method, an electronic device, and a medium. The method includes performing correction processing on a color source matrix of an acquired color chart image based on a color correction matrix to obtain an output color matrix; determining a global hue error and a saturation constraint regularization term based on Lab coordinates of the output color matrix and Lab coordinates of a target color matrix of a target color chart image and constructing a loss function; and updating the color correction matrix based on a value of the loss function until the value of the loss function converges to obtain an optimal color correction matrix to perform color correction on a to-be-processed image based on the optimal color correction matrix.

A method of generating learning data includes: generating a first code image that is a graphical code; generating a second code image by applying first image processing to the first code image; and generating a first image by superposing the second code image onto a background image.

Methods and systems are disclosed for generating an extended reality (XR) try-on experience based on an image produced by a diffusion model. The system receives an image depicting a real-world object and generates a prompt comprising a textual description of a fashion item. The system analyzes the image and the textual description of the fashion item using a generative machine learning model to generate an artificial image that depicts an artificial object that resembles the real-world object wearing an artificial fashion item matching the textual description of the fashion item. The system identifies an object comprising a real-world product image that matches visual attributes of the artificial fashion item and replaces the artificial fashion item in the artificial image with the object to generate an output image.