Generating color vector previews for images is described. A color vector preview system processes image data using parallel pipelines: one for determining a color palette based on an image frame's depicted content and another for recoloring image frames using the color palette. The color vector preview system replaces each pixel of an image frame with a color determined from a surrounding spatial neighborhood of pixels. Input specifying a number of colors is received, and the specified number of colors are selected from replaced pixel values to define the color palette. After determining the color palette, the color palette pipeline obtains a most-recently captured image frame and updates the color palette accordingly. Simultaneously, the recoloring pipeline compares each of the replaced pixel values to the color palette and replaces each pixel based on the comparison. Recolored frames are then output as color vector previews for the image data in real-time.

A method, computer program, and computer system is provided for video coding. Video data including one or more frames is received. A static background is estimated for each of the one or more frames based on a temporal average of the one or more frames. Pixels from among the one or more frames are identified as corresponding to the static background. Noise is removed in the static background based on the identified pixels.

A system and method for processing mammographic images of target breast tissue is provided. The mammographic images are processed to generate modified images. A deep learning algorithm, having a tailored Convolutional Neural Networks (CNN) model, is applied to the modified images to generate a first output and a second output. Global features associated with the entirety of the mammographic images are extracted by using the first output. Local features associated with Regions of Interest (ROIs) of the mammographic images are extracted by using the second output. The global features and the local features are combined and fuse to generate an indicator representative of likelihood of malignancy of the target breast tissue.

Presented is a wireless portable ultrasound acquisition system for dental imaging, having an ultrasound probe with a control switch connected through a cable to a portable ultrasound acquisition system that communicates wirelessly with a smart tablet or a phone display to display the ultrasound images. The system uses ultrasound signals to create images of alveolar bone structure and boundaries of enamel, dentin and gingiva of a patient.

A method of performing visualized measurement on thickness distribution of a paint film and an apparatus therefor. A measurement target region is heated by a heating unit that applies a light beam while moving relative to the measurement target region of a measurement target structure. A sensing unit moving together with the heating unit generates a plurality of thermal images related to a phenomenon in which thermal energy is propagated in the measurement target region by scanning and photographing the heated measurement target region. The thermal images in a dynamic state are converted into time-spatial-integrated thermal images in a static state by performing coordinate transformation according to a time-spatial-integrated coordinate transformation algorithm. A thickness of the paint film is calculated by using a Fourier thermal conduction equation. A noise caused by an external heat source is removed by subtracting a pre-heating time-spatial-integrated thermal image from the converted time-spatial-integrated thermal image.

An apparatus including processing circuitry configured to: acquire a plurality of sets of medical imaging data of a region of a subject, each set of data corresponding to a respective different measurement period; apply a filter to the plurality of medical imaging data sets to produce a plurality of filtered medical imaging data sets corresponding to the different measurement periods, wherein the applying of the filter is such that, for each of the medical imaging data sets, the filtering uses at least some information from the other medical imaging data sets acquired at the different time periods and wherein the applying of the filter comprises applying at least one constraint or condition and the constraint or condition comprises preserving at least one measure of intensity for each medical imaging data set.

A medical image processing method and apparatus, and an image processing method and apparatus, terminal and storage medium that obtains a to-be-processed medical image; generates a difference image according to the first image data, the second image data, and the third image data included in the to-be-processed medical image; and performs binarization processing on the difference image to obtain a binarized image, a foreground region of the binarized image corresponding to a pathological tissue region of the to-be-processed medical image. A difference image is generated based on color information of different channels before binarization processing is performed on an image, thereby effectively using the color information in the image. The pathological tissue region extracted based on the difference image is more accurate and facilitates subsequent image analysis.

An image processing device includes: a contribution ratio calculation unit that calculates a contribution ratio of a predetermined pixel or a predetermined region in depth calculation in each of a plurality of pixels or a plurality of regions included in an input image; and a correction unit that corrects a depth value of the predetermined pixel or the predetermined region based on the contribution ratio.

This neural network-type image processing device is provided with an input layer which comprises one unit where an input image is inputted, an output layer which comprises one unit where an output image is outputted, and multiple intermediate layers which are arranged between the input layer and the output layer and each of which comprises multiple units, the unit of the input layer, the units of the intermediate layers, and the unit of the output layer are fully connected with connection coefficients. The units of the intermediate layers are image processing modules which perform image processing on the image inputted to said units. The input image is inputted from the unit of the input layer, passes through the units of the intermediate layers, and is then outputted as an output image from the unit of the output layer; the connection coefficients are updated with learning based on a backpropagation algorithm.

Provided is a method for marking a focused pixel. The method includes: in response to marking a focused pixel of a first image based on a focus threshold, adjusting the focus threshold in the case that an adjustment condition is satisfied; marking a focused pixel of a second image based on the adjusted focus threshold. The adjustment condition includes: a focus degree of the first image is less than a target focus degree, and/or, the focus degree of the first image is greater than the target focus degree. Moreover, a similarity between the first image and the second image is greater than a similarity threshold, and a difference between a focus degree of the second image and the target focus degree is less than a difference between the focus degree of the first image and the target focus degree.