In learning processing performed before sample observation processing (steps S705 to S708), the sample observation device acquires a low-picture quality learning image under a first imaging condition for each defect position indicated by defect position information, determines an imaging count of a plurality of high-picture quality learning images associated with the low-picture quality learning image for each defect position and a plurality of imaging points based on a set value of the imaging count, acquires the plurality of high-picture quality learning images under a second imaging condition (step S702), learns a high-picture quality image estimation model using the low-picture quality learning image and the plurality of high-picture quality learning images (step S703), and adjusts a parameter related to the defect detection in the sample observation processing using the high-picture quality image estimation model (step S704).

A camera system comprising a body that contains a lens with a range of focus settings and an image sensor operable to record an image. The camera system has a controller operably connected to the sensor to receive the image, and the controller is operably connected to the lens to control the focus setting. The controller is operable to focus the lens on a selected point, and the controller is operable to determine at least two different first and second subject elements. The controller is operable to focus the lens on the first subject and record a first image, and the controller is operable to focus the lens on the second subject and record a second image.

Devices, methods, and non-transitory program storage devices are disclosed to provide enhanced synthetic Shallow Depth of Field (SDOF) images, e.g., by using information from images captured by image camera devices and one or more Deep Neural Networks (DNNs) trained to determine how much blurring should be applied to pixels in a mask region (e.g., a region of pixels having an indeterminate foreground or background status and threshold level of gradient magnitude) within an image, given context from surrounding pixels in a reference image and/or an unenhanced synthetic SDOF image. To train the DNNs, various sets of ground truth DSLR images of static scenes, captured at varying aperture settings, may be analyzed. Preferably, such static scenes cover many examples of human subjects (or other objects of interest) with different amounts of scene foreground/background separation, lighting conditions, and various kinds of hair, fabric, or other fine-grained details occurring near their foreground/background transition.

The image processing method according to the present application includes: acquiring a medical image captured by an imaging apparatus; and determining an intensity of a filter to be applied to the medical image according to a degree of focusing of the medical image.

In general, techniques are described regarding fusing or combining frames of image data to generate composite frames of image data. Cameras comprising camera processors configured to perform the techniques are also disclosed. A camera processor may capture multiple frames at various focal lengths. The frames of image data may have various regions of the respective frame in focus, whereas other regions of the respective frame may not be in focus, due to particular configurations of lens and sensor combinations used. The camera processor may combine the frames to achieve a single composite frame having both a first region (e.g., a center region) and a second region (e.g., an outer region) in focus.

An image processing apparatus includes an acquisition unit configured to acquire an image captured by an imaging unit, and image capturing information at the time of image capturing of the image, and a calculation unit configured to calculate an object side pixel dimension of a target subject in the image based on the image capturing information and a pixel dimension of the imaging unit, wherein the acquisition unit acquires in-focus information indicating an in-focus state of a subject in an image, as the image capturing information, and wherein the calculation unit calculates the object side pixel dimension based on the in-focus information.

Discussed herein are devices, systems, and methods for organ mask generation. A device, system and method for organ mask generation including generating a synthetic centroid mask, identifying first and second intensity thresholds, in a first segmentation pass, setting (i) pixels of an image with intensities less than the first threshold to zero and (ii) pixels of the image corresponding to objects with centroids outside the synthetic centroid mask to zero, resulting an initial organ mask, in a second segmentation pass, setting pixels (i) with intensities less than the second threshold, the second threshold less than the first threshold to zero and (ii) setting pixels corresponding to objects with centroids outside the initial organ mask to zero, resulting in a second organ mask, and expanding and filling the second organ mask to generate an organ mask.

A magnetic resonance (MR) imaging method of correcting nonuniformity in diffusion-weighted (DW) MR images of a subject is provided. The method includes applying a DW pulse sequence along a plurality of diffusion directions with one or more numbers of excitations (NEX), and acquiring a plurality of DW MR images of the subject along the plurality of diffusion directions with the one or more NEX. The method also includes deriving a reference image and a base image based on the plurality of DW MR images, generating a nonuniformity factor image based on the reference image and the base image, and combining the plurality of DW MR images into a combined image. The method also includes correcting nonuniformity of the combined image using the nonuniformity factor image, and outputting the corrected image.

A method for evaluating the effectiveness of a skin treatment on a skin feature includes correcting the color of a first image before the skin treatment, correcting the color of a second image after the skin treatment, determining the sizes of the skin feature in the first and second images, and comparing the corrected colors and sizes of the skin feature in the first and second images. In some embodiments, the first and second images include the skin feature and an indicator adjacent to the skin feature, where the indicator has a standard color and a known size.

An image processing apparatus includes: a memory; and a processor configured to execute one or more instructions stored in the memory to: determine a scale weight for each pixel of original pixels in an original image based on first attribute-related information set by dividing the original image into an object region and a peripheral region, respectively; determine an additional weight for at least one pixel of the original pixels in the peripheral region, based on the first attribute-related information, and second attribute-related information that is based on an amount of a change between pixel values of adjacent pixels; and obtain a transformed image of which a size is changed from the original image, by applying at least one of the scale weight and the additional weight to corresponding pixels of the original pixels to obtain a pixel value of a transformed pixel in the transformed image.