A method the following operations: downscaling an input image to generate a scaled image; performing, to the scaled image, a first convolutional neural networks (CNN) modeling process with first non-local operations, to generate global parameters; and performing, to the input image, a second CNN modeling process with second non-local operations that are performed with the global parameters, to generate an output image corresponding to the input image. A system is also disclosed herein.

A system and a method for removing haze from remote sensing images are disclosed. One or more hazy input images with at least four spectral channels and one or more target images with the at least four spectral channels are generated. The one or more hazy input images correspond to the one or more target images, respectively. A dehazing deep learning model is trained using the one or more hazy input images and the one or more target images. The dehazing deep learning model is provided for haze removal processing.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image segmentation algorithm (122). The image segmentation algorithm is configured for outputting one or more prede-termined anatomical regions within initial magnetic resonance imaging data (124) descriptive of a predetermined field of view (109) of a subject (318). The medical system further comprises a computational system (104), wherein execution of the machine executable in-structions causes the computational system to: receive (200) the initial magnetic resonance imaging data (124); receive (202) the image segmentation comprising the one or more anatomical regions within the magnetic resonance imaging data in response to inputting the initial magnetic resonance imaging data into the image segmentation algorithm; select (204) at least one of the one or more anatomical regions as a selected image portion (128) using a predetermined criterion; and reduce (206) image intensity within the selected image.

The present disclosure discloses a method and system for training a neural network for determining severity, and more particularly, a method and system which may effectively learn a neural network performing patch unit severity diagnosis using a pathological slide image to which a severity indication (label) is given.

This image quality evaluation device comprises: a conversion unit that converts the data of an image to two-dimensional array data of the luminance value of each pixel in the image; a processing unit that executes an averaging process, based on a plurality of filter sizes, on each pixel of the two-dimensional array data; and an evaluation unit that evaluates the quality of the image using the processing results of the processing unit.

A biological tissue is classified using a computing system. Image data comprising a plurality of images of an examination area of a biological tissue is received at the computing system. Each of the plurality of images is captured at different times during a period in which topical application of a pathology differentiating agent to the examination area of the tissue causes transient optical effects. The received image data is provided as an input to a machine learning algorithm operative on the computing system. The machine learning algorithm is configured to allocate one of a plurality of classifications to each of a plurality of segments of the tissue.

A system and method for reducing the responsibility of the user significantly by applying an optical system that can identify container like sample tubes with respect to their characteristics, e.g., shapes and inner dimensions, from their visual properties by capturing images from a rack comprising container and processing said images for reliably identifying a container tyle.

An image processing apparatus includes at least one processor. The processor is configured to execute region-of-interest image generation processing of generating a region-of-interest image from a projection image, which is obtained at an irradiation position closest to a position facing a detection surface of a radiation detector, among a series of projection images obtained by irradiating a breast with radiations and imaging the breast, and shape type determination processing of determining a type of a shape of a calcification image included in the region-of-interest image generated by the region-of-interest image generation processing.

Aspects presented herein relate to methods and devices for display processing including an apparatus, e.g., a DPU. The apparatus may receive at least one input image for a scaling operation, the at least one input image being associated with one or more scanning windows, each of the scanning windows including a plurality of pixels. The apparatus may also detect one or more features in the plurality of pixels in each of the one or more scanning windows. Further, the apparatus may adjust an amount of the plurality of pixels in each of the scanning windows for each of the detected features. The apparatus may also combine the adjusted amount of the plurality of pixels for each of the detected one or more features into a plurality of output pixels. The apparatus may also process each of the plurality of output pixels into at least one output image.

Apparatus for binning an input value into an array of bins, each bin representing a range of input values and the bins collectively representing a histogram of input values, the apparatus comprising: an input for receiving the input value; a memory for storing the array; and a binning controller configured to: derive a plurality of bin values from the input value according to a binning distribution located about the input value, the binning distribution spanning a range of input values and each bin value having a respective input value dependent on the position of the bin value in the binning distribution; and allocate the plurality of bin values to a plurality of bins in the array, each bin value being allocated to a bin selected according to the respective input value of the bin value.