A head-mountable display (HMD) system includes at least one detector to detect movement of an eye of a user wearing the HMD, control circuitry to detect saccadic movement of the eye in dependence upon the detected movement of the eye, a display unit and a processor to generate images for display to the user by the display unit, in which the control circuitry is configured to control the processor in response to the detected saccadic movement, in which the control circuitry is configured to control the processor to adjust one or more image quality parameters for one or more of the images to be displayed by the display unit during the detected saccadic movement.

The present disclosure discloses a method and a system for generating a composite PET-CT image based on a non-attenuation-corrected PET image. The method includes: constructing a first generative adversarial network and a second generative adversarial network; obtaining a mapping relationship between a non-attenuation-corrected PET image and an attenuation-corrected PET image by training the first generative adversarial network; obtaining a mapping relationship between the attenuation-corrected PET image and a CT image by training the second generative adversarial network; and generating the composite PET-CT image by utilizing the obtained mapping relationships. According to the present disclosure, a high-quality PET-CT image can be directly composited from a non-attenuation-corrected PET image, and medical costs can be reduced for patients, and radiation doses applied to the patients in examination processes can be minimized.

In a computer-implemented method for generating an image processing model that generates output data defining a stylized contrast image from a microscope image, model parameters of the image processing model are adjusted by optimizing at least one objective function using training data. The training data comprises microscope images as input data and contrast images, wherein the microscope images and the contrast images are generated by different microscopy techniques. In order for the output data to define a stylized contrast image, the objective function forces a detail reduction or the contrast images are detail-reduced contrast images with a level of detail that is lower than in the microscope images and higher than in binary images.

An image processing apparatus comprises a changing unit configured to change a display area of an image from a first display area to a second display area including at least a portion of the first display area, an acquiring unit configured to acquire a first value indicating luminance, in which brightness contrast is considered, in an image displayed in the first display area and a second value indicating luminance, in which brightness contrast is considered, in an image displayed in the second display area, and a correcting unit configured to correct luminance of the image displayed in the second display area based on the first value and the second value that are acquired by the acquiring unit.

An image processing system (10) according to an example aspect of the present disclosure includes: an image acquisition unit (102) configured to acquire a monochrome image; a pixel value correction unit (120) configured to correct a pixel value of the monochrome image based on information related to a pixel value; and a colorization generation unit (130) configured to generate a colorized image corresponding to the monochrome image from the corrected monochrome image by using a colorization prediction model trained by machine learning. With the present disclosure, it is possible to improve the reproduction accuracy of a color in colorization of a monochrome image.

In a learning device, a first learning unit performs first machine learning using first training data including a first evaluation result for an evaluation target image to generate a first learned model outputting a second evaluation result for an input image, an evaluation unit uses the first learned model to acquire a plurality of the second evaluation results for a plurality of the input images, a generation unit selects a second image quality parameter from a plurality of first image quality parameters having different values, based on the plurality of the second evaluation results and generates second training data including the selected second image quality parameter, and a second learning unit performs second machine learning using the second training data to generate a second learned model outputting a third image quality parameter used for processing a processing target image.

Methods, systems, and devices for equipment reading in a factory or plant environment are described, including: capturing an image of an environment including a measurement device; detecting a target region included in the image, the target region including at least a portion of the measurement device; determining identification information associated with the measurement device based on detecting the target region; and extracting measurement information associated with the measurement device based on detecting the target region. In some aspects, detecting the target region may include: providing the image to a machine learning network; and receiving an output from the machine learning network in response to the machine learning network processing the image based on a detection model, the output including the target region.

A single-shot differential phase contrast quantitative phase imaging method based on color multiplexing illumination. A color multiplexing illumination solution is used to realize single-shot differential phase contrast quantitative phase imaging. In the single-shot color multiplexing illumination solution, three illumination wavelengths of red, green, and blue are used to simultaneously illuminate a sample, and the information of the sample in multiple directions is converted into intensity information on different channels of a color image. By performing channel separation on this color image, the information about the sample at different spatial frequencies can be obtained. Such a color multiplexing illumination solution requires only one acquired image, thus enhancing the transfer response of the phase transfer function of single-shot differential phase contrast imaging in the entire frequency range, and achieving real-time dynamic quantitative phase imaging with a high contrast, a high resolution, and a high stability. In addition, an alternate illumination strategy is provided, so that a completely isotropic imaging resolution at the limit acquisition speed of the camera can be achieved.

One embodiment provides a computer-implemented method that includes providing a machine learning network including a global inverse tone mapping (GITM) structure and a local inverse tone mapping (LITM) structure that utilize one or more non-linear basis functions with one or more coefficient functions. The one or more non-linear basis functions learn linearly to facilitate combination with at least one convolution layer for jointly learning the machine learning network. A weighted mask (WM) is provided for reducing one or more visual artifacts, including one or more quantization artifacts in a smooth region of an output of the machine learning network.

According to certain embodiments, an electronic device comprises a display; a camera disposed under the display; and a processor configured to, obtain a first image using the camera in a first state in which a first portion of the display corresponding to a position where the camera is disposed is in a first mode, obtain a second image, using the camera in a second state in which the first portion of the display is in a second mode, calculate correction values, based on first data for a first area of the first image and second data for a second area of the second image, wherein the second area of the second image corresponds to the first area of the first image, and correct at least one image among a plurality of images, using the correction values, wherein one of obtaining the first image and obtaining the second image is responsive to the other of obtaining the first image and the second image.