A system for displaying medical imaging data comprising one or more data inputs, one or more processors, and one or more displays, wherein the one or more data inputs are configured for receiving first image data generated by a first medical imaging device, wherein the first image data comprises a field of view (FOV) portion and a non-FOV portion, and the one or more processors are configured for identifying the non-FOV portion of the first image data and generating cropped first image data by removing at least a portion of the non-FOV portion of the first image data, and transmitting the cropped first image data for display in a first portion of the display and additional information for display in a second portion of the display.

An electronic apparatus may include a memory that stores first information regarding a plurality of first artificial intelligence models trained to perform image processing differently from each other and second information regarding a second artificial intelligence model trained to identify a type of an image by predicting a processing result of the image by each of the plurality of first artificial intelligence models. The electronic apparatus may further include a processor configured to identify a type of an input image by inputting the input image to the second artificial intelligence model stored in the memory, and process the input image by inputting the input image to one of the plurality of first intelligence models stored in the memory based on the identified type.

A method of training a generator G of a Generative Adversarial Network (GAN) includes receiving, by an encoder E, a target data Y; receiving, by the encoder E, an output G(Z) of the generator G, where the generator G generates the output G(Z) in response to receiving a random sample Z and where a discriminator D of the GAN is trained to distinguish which of the G(Z) and the target data Y; training the encoder E to minimize a difference between a first latent space representation E(G(Z)) of the output G(Z) and a second latent space representation E(Y) of the target data Y, where the output G(Z) and the target data Y are input to the encoder E; and using the first latent space representation E(G(Z)) and the second latent space representation E(Y) to constrain the training of the generator G.

An image processing apparatus for performing image quality processing on an image includes a feature extraction network and an image quality processing network including one or more modulation blocks, wherein each of the one or more modulation blocks includes a convolution layer, a modulation layer, and an activation layer for processing the image.

An image enhancement system and method based on a generative adversarial network (GAN) model. The image enhancement system includes an acquiring unit, a training unit and an enhancement unit. The acquiring unit is configured to acquire a first image of a driving environment captured by a camera of a first vehicle and a second image of the driving environment captured by a camera of a second vehicle. The training unit is configured to train a GAN by using the first training image to obtain an image enhancement model. The enhancement unit is configured to enhance the second image by inputting the second image into the image enhancement model.

A method of substantially real-time image restoration of an infrared camera includes the steps of: analyzing the last X number of video frames; classifying the last X number of video frames as to the source of noise in the last X number of video frames; selecting a noise suppression transform based on the source of the noise; receiving real time video frames; correcting the real time video frames using the selected noise suppression transform.

The present disclosure relates to a video repair method and apparatus, an electronic device, and a storage medium. The method includes: acquiring first forward optical flows and first reverse optical flows between adjacent images among continuous multiple frames of images; respectively performing optical flow optimization processing on the first forward optical flows and the first reverse optical flows to obtain second forward optical flows corresponding to the first forward optical flows and second reverse optical flows corresponding to the first reverse optical flows; performing forward and reverse conduction optimization on the continuous multiple frames of images by utilizing the second forward and the second reverse optical flows, respectively, until all the images in the optimized continuous multiple frames of images satisfy repair requirements; and obtaining repaired images of the continuous multiple frames of images according to the optimized images obtained by the forward conduction optimization and the reverse conduction optimization.

This invention is related to a method for enhancing the contrast and other image quality aspects of an electronic representation of an image that is based on a multi-scale decomposition and recomposition method, wherein the image enhancement steps involve the processing of the detail images by a conversion function, which is optimized by the algorithm itself by means of optimizing the defining parameters of this conversion function by a cost-function based optimization.

An image processor receives first image data representing an image. The first image data comprising a plurality of color values corresponding to a plurality of pixels in the image. The image processor determines, using a trained machine learning model, second image data based on the first image data. The second image data comprises surface spectral reflection values corresponding to the plurality of pixels in the image, where the surface spectral reflection values are distributed across a plurality of wavelengths of visible light in the image. The image processor then performs at least one image processing operation with respect to the image using the second image data.

A framework for multi-scan image processing. A single real anatomic image of a region of interest is first acquired. One or more emission images of the region of interest are also acquired. One or more synthetic anatomic images may be generated based on the one or more emission images. One or more deformable registrations of the real anatomic image to the one or more synthetic anatomic images are performed to generate one or more registered anatomic images. Attenuation correction may then be performed on the one or more emission images using the one or more registered anatomic images to generate one or more attenuation corrected emission images.