An apriori guidance network for multitask medical image synthesis is provided. The apriori guidance network includes a generator and a discriminator, wherein the generator includes an apriori guidance module configured to convert an input feature map into a target modal image pointing to a target domain according to an apriori feature, and the apriori feature is a deep feature of the target modal image. The generator is configured to generate a corresponding target domain image by taking the apriori feature of the target modal image and source modal image data as an input. The discriminator is configured to discriminate an authenticity of the target domain image outputted by the generator.

The present disclosure generally relates to the field of deep learning technologies. An apparatus for generating a plurality of correlation images may include a feature extracting unit configured to receive a training image and extracting at least one or more of feature from the training image to generate a first feature image based on the training image; a normalizer configured to normalize the first feature image and generate a second feature image; and a shift correlating unit configured to perform a plurality of translational shifts on the second feature image to generate a plurality of shifted images, correlate each of the plurality of shifted images with the second feature image to generate the plurality of correlation images.

An electronic device includes at least one imaging sensor and at least one processor coupled to the at least one imaging sensor. The at least one imaging sensor is configured to capture a burst of image frames. The at least one processor is configured to generate a low-resolution image from the burst of image frames. The at least one processor is also configured to estimate a blur kernel based on the burst of image frames. The at least one processor is further configured to perform deconvolution on the low-resolution image using the blur kernel to generate a deconvolved image. In addition, the at least one processor is configured to generate a high-resolution image using super resolution (SR) on the deconvolved image.

The disclosed system modifies luminance of a display associated with a selective screen. The display provides a camera with an image having resolution higher than the resolution of the display by presenting multiple images while the selective screen enables light from different portions of the multiple images to reach the camera. The resulting luminance of the recorded image is lower than a combination of luminance values of the multiple images. The processor obtains a criterion indicating a property of the input image where image detail is unnecessary. The processor detects a region of the input image satisfying the criterion, and determines a region of the selective screen corresponding to the region of the input image. The processor increases the luminance of the display by disabling the region of the selective screen corresponding to the region of the input image.

A method includes receiving a three-dimensional image dataset of a surgical site of a patient. The method also includes segmenting one or more anatomical features of the surgical site based on the three-dimensional image dataset. The method also includes receiving a two-dimensional image of the surgical site of the patient and registering the two-dimensional image to an image from the three-dimensional image dataset. The method also includes displaying a two-dimensional representation of the segmented one or more anatomical features based on the registered two-dimensional image and the image from the three-dimensional image dataset.

Examples of the disclosure describe systems and methods for recording augmented reality and mixed reality experiences. In an example method, an image of a real environment is received via a camera of a wearable head device. A pose of the wearable head device is estimated, and a first image of a virtual environment is generated based on the pose. A second image of the virtual environment is generated based on the pose, wherein the second image of the virtual environment comprises a larger field of view than a field of view of the first image of the virtual environment. A combined image is generated based on the second image of the virtual environment and the image of the real environment.

An imaging system includes a processor, a memory, a visible light camera configured to record a first image of a scene, and an infrared camera configured to record a second image of the scene. The processor configured to execute instructions stored in the memory to input the first image and the second image into a neural network. The neural network relights the first image, based on characteristics of the second image, to correspond to an image of the scene under canonical illumination conditions.

A method and apparatus for enhancing magnetic resonance images to produce contrast-enhanced images without the need to administer contrast agent to a patient. The image processing apparatus utilises a trained machine learning algorithm as an image processor, preferably a generative adversarial network, to produce images from contrast agent-free magnetic resonance images with the produced images having similar appearance and better image quality and better pathological sensitivity and being able to differentiate more pathological conditions than actually acquired contrast-enhanced images.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain contrast-enhanced images related to an examined subject and corresponding to a plurality of temporal phases. On the basis of data values of pixels in the contrast-enhanced images corresponding to the plurality of temporal phases, the processing circuitry is configured to determine which one of contrast enhancement dominance and fluid accumulation dominance corresponds to the pixels or a region including the pixels. The processing circuitry is configured to generate a display mode based on the determination.

The present disclosure includes systems, methods and media for rendering objects translucent and for recovery of anatomical information blocked by the objects in medical images.