This disclosure relates to image processing method and apparatus. The method includes: processing, with a first generator in an image processing model, a first sample image in a first sample set to obtain a first predicted image; processing, with the first generator, a second sample image in a second sample set to obtain a second predicted image; and training the image processing model according to a difference between the target avatar in the first sample image and the first predicted avatar and a difference between a first type attribute of the sample avatar in the second sample image and a first type attribute of the second predicted avatar.

A method (400) and a system (200) for generating a chromatically modified image of one or more components on a microscopic slide (303) is disclosed. In one aspect of the invention, the method includes obtaining the image of the one or more components on the microscopic slide (303). Additionally, the method (400) includes processing the image to identify the one or more components. The method (400) further includes segmenting at least one part of the one or more components identified from the image. Furthermore, the method (400) includes chromatically modifying the at least one part of the one or more components and generating a chromatically modified image of the one or more components.

A method for medical image data enhancement is provided. The method includes: receiving a medical image sample set related to an object to be detected; based on an attribute of the object lacking in the medical image sample set, selecting a first medical image and a second medical image from the medical image sample set, where the first medical image contains the object lacking the attribute, and the second medical image does not contain the object lacking the attribute; determining a first area image block containing the lacking attribute; determining a second area image block not containing the lacking attribute; generating a composite area image block by fusing the first area image block and the second area image block based on a mask including an object part and a peripheral part around the object part; embedding the composite area image block back into the second medical image to obtain a third medical image; including the third medical image in the medical image sample set to obtain a data-enhanced medical image sample set.

A method for colourising a three-dimensional point cloud including surveying a point cloud with a surveying instrument. Each point of the point cloud may be characterised by coordinates within an instrument coordinate system having an instrument center. The method may include capturing a first image of the setting with a first camera. Each pixel value of the first image is assigned coordinates within a first camera coordinate system having a first projection center as origin and a first parallax shift relative to the instrument center. The method may include transforming the point cloud from the instrument coordinate system into the first camera coordinate system, resulting in a first transformed point cloud, detecting one or more uncovered points within the first transformed point cloud which are openly visible from the first projection center, and for each uncovered point, assigning a pixel value having corresponding coordinates in the first camera coordinate system.

Processing for judging whether a face is included in a frame is performed, in a predetermined interval, on each of frames included in a moving image of a subject, displayed on a monitor, until the judgment becomes positive. If it is judged that a face is included in a frame, the facial position is detected in the frame, and stored. Then, judgment is made as to whether a face is included in the next frame after predetermined time. If the judgment is positive, the facial position is detected. The previously stored facial position is replaced by the newly detected facial position, and the newly detected facial position is stored. These processes are repeated until photographing operation is performed by operating a release unit.

An image processing apparatus includes a first image generator and a second image generator. The first image generator generates a first image, including a predetermined ruled-line image and an inscription image, from a second sheet in a sheet group. The sheet group is obtained by stacking multiple sheets including a single first sheet and the second sheet. The first sheet has inscription information inscribed thereon. The second sheet has the inscription image corresponding to the inscription information transferred thereon and includes the ruled-line image. The second image generator generates a second image in which a surplus image is removed from the first image generated by the first image generator in accordance with a learning model that has learned to remove the surplus image different from the ruled-line image and the inscription image.

In one embodiment, a method includes accessing a plurality of image frames captured by one or more cameras, classifying one or more first objects detected in one or more first image frames of the plurality of image frames as undesirable, applying a pixel filtering to the one or more first image frames to replace one or more first pixel sets associated with the one or more first objects with pixels from one or more second image frames of the plurality of image frames to generate a final image frame, providing the final image frame for display.

Provided are a video processing method and apparatus, an electronic device, a storage medium and a program product. The method includes steps described below. A to-be-processed video is acquired, and a first face key point of a first target object in the to-be-processed video is recognized, where the first face key point corresponds to a mask key point in a three-dimensional human face mask special effect; and the three-dimensional human face mask special effect is added to video frames of the to-be-processed video to obtain a target video, where the mask key point in the three-dimensional human face mask special effect moves with the corresponding first face key point.

An image calibration method applied to a wide-angle image and executed by an image calibration apparatus includes applying primary lens distortion correction for the wide-angle image to generate a corrected image, segmenting an foreground image from the corrected image to generate a background image, applying secondary distortion correction for the foreground image based on the pre-defined object to generate a calibrated foreground image, fusing the background image with the calibrated foreground image to generate a fused image, detecting at least one residual empty pixel not overlapped by the calibrated foreground image within the fused image, and utilizing a machine learning algorithm to fill the at least one residual empty pixel of the fused image by extending the background image to provide an output image. The foreground image contains feature pixels relate to a pre-defined object and the background image has empty pixels corresponding to the foreground image.

Systems and methods for image processing are described. Embodiments of the present disclosure identify a first image depicting a first object; identify a plurality of candidate images depicting a second object; select a second image from the plurality of candidate images depicting the second object based on the second image and a sequence of previous images including the first image using a crop selection network trained to select a next compatible image based on the sequence of previous images; and generate a composite image depicting the first object and the second object based on the first image and the second image.