Described herein is a method for training a denoising model. The method includes obtaining a first set of simulated images based on design patterns. The simulated images may be clean and can be added with noise to generate noisy simulated images. The simulated clean and noisy images are used as training data to generate a denoising model.

An image processing device 100 includes, in a case where designation of a plurality of partial regions corresponding to a cell morphology is received, the plurality of partial regions being extracted from a pathological image, a generation unit 154 that generates auxiliary information indicating information about a feature amount effective when a plurality of partial regions is classified or extracted with respect to a plurality of feature amounts calculated from the image; and in a case where setting information about an adjustment item according to the auxiliary information is received, an image processing unit 155 that performs an image process on the image using the setting information.

An image display method includes the following operations (a) to (e). The (a) is of obtaining a plurality of two-dimensional images by two-dimensionally imaging a specimen, in which a plurality of objects to be observed are present three-dimensionally in the specimen, at a plurality of mutually different focus positions. The (b) is of obtaining image data representing a three-dimensional shape of the specimen. The (c) is of obtaining a three-dimensional image of the specimen based on the image data. The (d) is of obtaining the two-dimensional image selected from the plurality of two-dimensional images or a two-dimensional image generated to be focused on the plurality of objects based on the plurality of two-dimensional images as an integration two-dimensional image. The (e) is of integrating the integration two-dimensional image obtained in the (d) with the three-dimensional image obtained in the (c) and displaying an integrated image on a display unit.

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.

Systems and methods herein provide improved, high-throughput multiphoton imaging of thick samples with reduced emission scattering. The systems and methods use structured illumination to modify the excitation light. A reconstruction process can be applied to the resulting images to recover image information free of scattering. The disclosed systems and methods provide high throughput, high signal-to-noise ratio, and high resolution images that are depth selective.

Systems and methods herein provide improved, high-throughput multiphoton imaging of thick samples with reduced emission scattering. The systems and methods use structured illumination to modify the excitation light. A reconstruction process can be applied to the resulting images to recover image information free of scattering. The disclosed systems and methods provide high throughput, high signal-to-noise ratio, and high resolution images that are depth selective.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Condensation associated with the collection and identification of airborne particles is detected. Upon the detection, one or more condensation countermeasures are triggered to address the condensation.

A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the image into a region of interest that includes information useful for classification and a background region by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.

Disclosed herein include embodiments of a system, a device, and a method for sorting a plurality cells of a sample. A plurality of raw images comprising pixels of complex values in a frequency space can be generated from a plurality of channels of fluorescence intensity data of fluorescence emissions of fluorophores, the fluorescence emissions being elicited by fluorescence imaging using radiofrequency-multiplexed excitation in a temporal space. Spectral unmixing can be performed on the raw images prior to a sorting decision being made.