An ultrasound diagnosis apparatus according to an embodiment includes processing circuitry and a display. The processing circuitry generates, from ultrasound data, pieces of low and high frequency data corresponding to resolutions of gradual degrees, extracts pieces of negative high frequency data over a range of predetermined resolutions, from the pieces of high frequency data, restores the resolution of first addition data so as to be equal to the resolution of the ultrasound data before reduction, the first addition data being obtained by adding up the pieces of negative high frequency data while causing resolutions to match over the range of the predetermined resolutions, and generates a combined image on the basis of the restored first addition data and the ultrasound data. The display displays the combined image.

Cerebrospinal fluid (CSF) drainage systems. A system includes a conduit having a proximal end and a distal end. The conduit receives the CSF from a patient from the proximal end. The system includes a collection chamber coupled to the distal end. The collection chamber collects the CSF. The system includes a valve positioned on the conduit. The valve controls CSF flow into the collection chamber. The system includes a camera that captures an image of the CSF within the collection chamber. The system includes a processor coupled to the camera. The processor measures a flow rate of the CSF based on the image and controls the first valve to open for a first predetermined period and close for a second predetermined period until a determination of a predetermined amount of the CSF being drained from the patient is made by the processor based on the flow rate.

A color image inpainting method includes: obtaining a color image of an object to be recognized, the color image including a missing portion where at least part of image information is missing; obtaining an infrared image of the object; identifying the missing portion in the color image; and inpainting the missing portion in the color image identified in the identifying. The inpainting includes inpainting the missing portion by using information which is obtained from the infrared image and corresponds to the missing portion to obtain an inpainted color image of the object.

A method, device, and computer program product are designed for non-convolutional image processing in microscopy of an input image into an output image using an artificial neural network with at least one contracting path including layers, at least one expanding path including layers, and at least one filter kernel. The method includes determining, in one or multiple artificial neural network layers, a similarity metric between at least one filter kernel and one output of the previous layer. Additionally, in at least one layer of the contracting path, the resolution of the output of the previous layer is reduced, and, in at least one layer of the expanding path, the resolution of the output of the previous layer is increased. The first artificial neural network layer treats the input image as the output of the previous layer, and the output of the last artificial neural network layer is the output image.

A method and apparatus for adaptive decoding of compressed video for video conferencing may be provided. The method may include receiving compressed video data comprising a plurality of video frames, and determining, a selection signal indicating whether at least one of a face restoration technique and a face reenactment technique is to be used. The method may include adaptively selecting and transmitting a single reference frame or a plurality of low resolution (LR) frames comprising essential facial features, generating, one or more recovered facial features and one or more respective decompressed low resolution (LR) extended face areas based on the selection signal and the compressed video data, and decoding a video frame from the plurality of video frames based on the one or more recovered facial features and the one or more respective decompressed low resolution (LR) extended face areas.

A method for developing an enhancement model for low-quality visual data, the method comprising the steps of receiving one or more sections of higher-quality visual data; and training a hierarchical algorithm. The hierarchical algorithm is operable to increase the quality of one or more sections of lower-quality visual data so as to substantially reproduce the one or more sections of higher-quality visual data. The hierarchical algorithm is then outputted.

Methods and apparatuses for video transcoding based on spatial or temporal importance include: in response to receiving an encoded video bitstream, decoding a picture from the encoded video bitstream; determining a first level of spatial importance for a first region of a background of the picture based on an image segmentation technique; applying to the first region a first resolution-enhancement technique associated with the first level of spatial importance for increasing resolution of the first region by a scaling factor, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels; and encoding the first region using a video coding standard.

An image processing component is provided. The apparatus includes: a display control configured to display an image to be edited in a first region of a display region or display a target image obtained from an image to be edited; an automatic image processing control configured to enter an automatic mode in response to a first control instruction; and a manual image processing control configured to enter a manual mode in response to a second control instruction. In the automatic mode and/or manual mode, the electronic device can switch between different graph repairing functions in response to a function switching instruction.

A method, a device, and a computer program product captures microscopy objects in image data that includes first images recorded with a first contrast and second images recorded with a second contrast, wherein in each case, one of the first and one of the second images can be correspondingly assigned to each other. The method includes capturing information indicating microscopy objects in at least one of the second images, transferring the captured information to those of the first images which correspond to the at least one of the second images, and capturing information indicating microscopy objects in the first images, to which the captured information of the second images was transferred by using the transferred information.

An image annotation method for an image annotation system is provided. The image annotation method includes the following steps. Firstly, an original image is provided. Then, an image pre-processing process is performed on the original image to generate an adjusted image. Then, the adjusted image is inferred according to a deep learning model, so that at least one predicted result is obtained. Then, an image post-processing process is performed on the adjusted image and the at least one predicted result to generate a final image. Then, the final image, the at least one predicted result and at least one annotation of the at least one predicted result are displayed.