A matching transformation is determined for matching a patient image set of images of an anatomical body structure of a patient with an atlas image set of images of a general anatomical structure including anatomical atlas elements. Atlas spatial information containing spatial information on the general anatomical structure, and element representation information are obtained. The element representation information describes representation data sets which contain information on representations of the plurality of atlas elements in the atlas images to be determined are obtained, and also describes a determination rule for determining respective representation data sets for respective atlas elements in accordance with different respective parameter sets. Patient data is acquired by acquiring the patient image set and the parameter sets which are respectively associated with the images of the patient image set. The matching transformation is determined by matching images associated with the same parameter set to each other.

This specification describes a method including determining a resolution of visual source content, determining a resolution and a field of view of a display of a virtual reality headset, determining, based at least in part on the resolution of the visual source content; the resolution of the display; and the field of view of the display: a field of view for display of visual data corresponding to the visual source content in the virtual reality space, and a projection shape for display of the visual data corresponding to the visual source content in the virtual reality space, the projection shape including one of a flat projection, a horizontally curved projection, a vertically curved projection, a spherical projection, and a combination thereof.

Systems and methods for use in training a convolutional neural network (CNN) for image and video transformations. The CNN is trained by adding noise to training data set images, transforming both the noisy image and the source image, and then determining the difference between the transformed noisy image and the transformed source image. The CNN is further trained by using an object classifier network and noting the node activation levels within that classifier network when transformed images (from the CNN) are classified. By iteratively adjusting the CNN to minimize a combined loss function that includes the differences between the node activation levels for the transformed references images and when transformed source are classified and the differences between the transformed noisy image and the transformed source image, the artistic style being transferred is maintained in the transformed images.

A matching transformation is determined for matching a patient image set of images of an anatomical body structure of a patient with an atlas image set of images of a general anatomical structure including anatomical atlas elements. Atlas spatial information containing spatial information on the general anatomical structure, and element representation information are obtained. The element representation information describes representation data sets which contain information on representations of the plurality of atlas elements in the atlas images to be determined are obtained, and also describes a determination rule for determining respective representation data sets for respective atlas elements in accordance with different respective parameter sets. Patient data is acquired by acquiring the patient image set and the parameter sets which are respectively associated with the images of the patient image set. The matching transformation is determined by matching images associated with the same parameter set to each other.

A system can receive downhole acquisition data relating to a wellbore. The system can pre-process the downhole acquisition data. The system can generate an incomplete borehole image using the downhole acquisition data. The system can determine a sparse representation based on the incomplete borehole image by performing an optimization with respect to the incomplete borehole image. The system can generate a complete borehole image based on an inverse of the sparse representation.

A system can receive downhole acquisition data relating to a wellbore. The system can pre-process the downhole acquisition data. The system can generate an incomplete borehole image using the downhole acquisition data. The system can determine a sparse representation based on the incomplete borehole image by performing an optimization with respect to the incomplete borehole image. The system can generate a complete borehole image based on an inverse of the sparse representation.

Provided are an image processing method and apparatus, a storage medium, and a device. The method includes acquiring edge image information in an expansion direction of an original image, selecting a target expansion mode from at least two candidate expansion modes according to the edge image information, and processing the original image by using the target expansion mode to obtain a target image.

Provided are an image processing method and apparatus, a storage medium, and a device. The method includes acquiring edge image information in an expansion direction of an original image, selecting a target expansion mode from at least two candidate expansion modes according to the edge image information, and processing the original image by using the target expansion mode to obtain a target image.

The image processing device 1X includes an acquisition means 31X, a selection means 32X, and a determination means 33X. The acquisition means 31X is configured to acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by a photographing unit provided in an endoscope. The selection means 32X is configured to select partial data that is a part of the data. The determination means 33X is configured to make a determination regarding an attention point to be noticed in the examination target based on the partial data. It can be used for assisting user's decision making,

An image matching apparatus matching a first image against a second image includes an acquiring unit, a generating unit, and a determining unit. The acquiring unit acquires a frequency feature of the first image and a frequency feature of the second image. The generating unit synthesizes the frequency feature of the first image and the frequency feature of the second image, and generates a quantized synthesized frequency feature in which a value of an element is represented by a binary value or a ternary value. The determining unit calculates a score indicating a degree to which the quantized synthesized frequency feature is a square wave having a single period, and matches the first image against the second image based on the score.