Some example embodiments relate to a super resolution scanning electron microscope (SEM) image implementing device and/or a method thereof. Provided a super resolution scanning electron microscope (SEM) image implementing device comprising a processor configured to crop a low resolution SEM image to generate a first cropped image and a second cropped image, to upscale the first cropped image and the second cropped image to generate a first upscaled image and a second upscaled image, and to cancel noise from the first upscaled image and the second upscaled image to generate a first noise canceled image and a second noise canceled image.

Disclosed is a ray clustering learning method based on weakly-supervised learning for denoising using ray tracing. The ray clustering learning method is for learning a denoising model for removing noise from a rendered image through ray tracing, and includes extracting a feature of a simulated ray through the ray tracing and clustering the ray through contrastive learning for the feature.

Disclosed are an image processing method and apparatus, a computer device and a storage medium, which relate to the field of artificial Intelligence. The method includes receiving an original image; performing image photographing defect detection and color deviation detection on the original image, the image photographing defect detection determining whether there exists a photographing defect that is irreparable through image processing, the color deviation detection determining whether there exists color cast in the original image; performing color correction on the original image if the original image passes the image photographing defect detection and does not pass the color deviation detection; and generating a target image based on the color-corrected original image.

An image is received from a camera built into a cabin of a vehicle. The image is demosaiced and its noise is reduced. A segmentation algorithm is applied to the image. A global illumination for the image is solved. Based on the segmentation of the image and the global illumination, a bidirectional reflectance distribution function (BRDF) for color and/or reflectance information of material in the cabin area of the vehicle is solved for. A white balance matrix and a color correction matrix for the image are computed based on the BRDF. The white balance matrix and the color correction matrix are applied to the image, which is then displayed or stored for addition image processing.

A method of an electronic device including obtaining a low-resolution input image by down-sampling a high-resolution input image; obtaining a low-resolution output image by performing image quality processing on the low-resolution input image; obtaining a low-resolution model from a conversion relationship between the low-resolution input image prior to the image quality processing being performed and the low-resolution output image subsequent to the image quality processing being performed; performing up-sampling of the low-resolution model; obtaining a high-resolution model by modifying the up-sampled low-resolution model, based on a difference between the high-resolution input image and the low-resolution input image; and obtaining a high-resolution output image from the high-resolution input image, by applying the high-resolution model to the high-resolution input image.

A method, performed by an augmented reality (AR) device including a vision correction lens, of detecting a gaze of a user is provided. The method includes obtaining lens characteristic information about the vision correction lens arranged to overlap a light guide plate in a gaze direction of the user, emitting light for gaze tracking toward a light reflector through a light emitter, wherein the emitted light is reflected by the light reflector and then directed to an eye of the user, receiving a light reflected by the eye of the user through a light receiver, obtaining an eye image of the user based on the light received, adjusting the eye image of the user based on the lens characteristic information about the vision correction lens, and obtaining gaze information based on the adjusted eye image.

One embodiment provides a method generating a first image crop and a second image crop randomly extracted from a low-quality image and a high-quality image, respectively. The method further comprises comparing the first image crop and the second image crop using a plurality of loss functions including pixel-wise loss to calculate losses, and optimizing a model trained to estimate a realistic scale-independent blur kernel of a low-resolution (LR) blurred image by minimizing the losses.

A receiving-side apparatus executes: reception processing for receiving a camera image from a transmitting-side apparatus; determination processing for determining whether execution conditions for performing image quality improvement processing with respect to the camera image are established; in a case where the execution conditions are established, order processing for transmitting the camera image to an external server apparatus connected through a network to the receiving-side apparatus, and ordering the image quality improvement processing with respect to the camera image; processing for receiving an image-quality-improved image generated by performing image quality improvement processing on the camera image from the external server apparatus; and processing for displaying the image-quality-improved image on a display apparatus.

Embodiments of the present disclosure provide a method and apparatus for processing a video frame, and relates to the field of computer vision technology. The method may include: acquiring a plurality of candidate first-order radial distortion parameters preset for a to-be-processed video frame, and acquiring a specified value of a specified radial distortion parameter; performing radial distortion correction on the to-be-processed video frame to obtain a first initial corrected video frame; selecting a first initial corrected video frame in which a local region except for a center region after distortion correction includes a largest number of straight line segments; and determining a candidate first-order radial distortion parameter corresponding to the selected first initial corrected video frame for use as a target first-order radial distortion parameter of the to-be-processed video frame.

The ultrasonic diagnostic apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to: acquire multiple position data associated with respective multiple two-dimensional image data of ultrasonic related to multiple cross sections; smooth the acquired multiple position data; and arrange the multiple two-dimensional image data in accordance with the smoothed multiple position data to generate volume data.