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.

An image processing method and apparatus, and a non-transitory computer-readable storage medium are provided. The method includes: acquiring an exposure duration and at least one motion component corresponding to at least one direction within the exposure duration of a current video frame; performing a first noise reduction operation on the current video frame in a two-dimensional space domain in response to a first motion component of the at least one motion component being greater than a preset motion component threshold; and performing a second noise reduction operation on the current video frame in a three-dimensional space domain in response to the at least one motion component being less than the preset motion component threshold.

The disclosure discloses a cartoonlization processing method for an image, and relates to a field of computational vision, image processing, face recognition, deep learning technologies. The method includes: performing skin color recognition on a facial image to be processed to determine a target skin color of a face in the facial image; processing the facial image by utilizing any cartoonizing model in a cartoonizing model set to obtain a reference cartoonized image corresponding to the facial image in a case that the cartoonizing model set does not contain a cartoonizing model corresponding to the target skin color; determining a pixel adjustment parameter based on the target skin color and a reference skin color corresponding to the any cartoonizing model; and adjusting a pixel value of each pixel point in the reference cartoonized image based on the pixel adjustment parameter, to obtain a target cartoonized image corresponding to the facial image.

A binary image is generated from a multivalued image based on a threshold, an edge is extracted from the multivalued image, an edge image is generated by correcting a position of the extracted edge, and a synthetic binary image is generated by synthesizing the edge image and the binary image.

A solution to the problem of image and video stitching is disclosed that compensates for the effects of lens distortion, camera misalignment, and parallax in combining multiple images. The disclosed image stitching technique includes depth or disparity estimation, alignment, and blending processes configured to be computationally efficient and produce quality results by limiting the presence of noticeable seams and artifacts in the final stitched image. An inter-frame approach applies image stitching to video frames to maintain temporal continuity between successive frames across a stitched video output having a 360-degree viewing perspective. A temporal adjustment is configured to improve temporal continuity between a subsequent frame and a previous frame in a sequence of video frames.

Facial biometric systems and methods employ digital fingerprints that represent facial characteristics. These systems and methods may perform various digital fingerprint related processes including induction (acquiring of the facial data, generation of a digital fingerprint and storing in a database) and authentication (a process of using the digital fingerprint of the face to perform biometrics). The facial biometric system and method combines these processes with a particular set of hardware to perform the facial biometrics process that is novel and provides benefits and capabilities not achievable by other biometric systems and methods.

Methods, devices, and systems for identifying charging devices are provided. In one aspect, a method of identifying a charging device include: capturing an infrared image and a depth image of a current field of view with a depth camera; determining, according to the infrared image, that there are one or more suspected charging device areas that satisfy first specified conditions; determining, according to the depth image, that there is a target charging device area whose height relative to a depth camera is within a specified range in the one or more suspected charging device areas; and identifying the charging device according to the target charging device area. The first specified conditions indicate that a gray-scale value of each of pixels in an area is greater than a second specified value, and a number of the pixels in the area is greater than a third specified value.

Various methods and systems are provided for x-ray imaging. In one embodiment, a method for an image pasting examination comprises acquiring, via an optical camera and/or depth camera, image data of a subject, controlling an x-ray source and an x-ray detector according to the image data to acquire a plurality of x-ray images of the subject, and stitching the plurality of x-ray images into a single x-ray image. In this way, optimal exposure techniques may be used for individual acquisitions in an image pasting examination such that the optimal dose is utilized, stitching quality is improved, and registration failures are avoided.

A dual sensor imaging system and an imaging method thereof are provided. The method includes: identifying an imaging scene; controlling a color sensor and an IR sensor to respectively capture color images and IR images by adopting capturing conditions suitable for the imaging scene; calculating a signal-to-noise ratio (SNR) difference between each color image and the IR images, and a luminance mean value of each color image; selecting the color image and IR image captured under capturing conditions of having the SNR difference less than an SNR threshold and the luminance mean value greater than a luminance threshold to execute a feature domain transformation to extract partial details of the imaging scene; and fusing the selected color image and IR image to adjust the partial details of the color image according to a guidance of the partial details of the IR image to obtain a scene image with full details.

A dynamic anatomic atlas is disclosed, comprising static atlas data describing atlas segments and dynamic atlas data comprising information on a dynamic property which information is respectively linked to the atlas segments.