Embodiments relate to axial chromatic aberration (ACA) reduction of raw image data generated by image sensors. A chromatic aberration reduction circuit performs chromatic aberration reduction on the raw image data to correct the ACA in the full color images through sharpening that has been clamped to reduce sharpening overshoot.

The present disclosure relates to a solid-state imaging device, a signal processing method therefor, and an electronic apparatus enabling sensitivity correction in which a sensitivity difference between solid-state imaging devices is suppressed. The solid-state imaging device includes a pixel unit in which one microlens is formed for a plurality of pixels in a manner such that a boundary of the microlens coincides with boundaries of the pixels. The correction circuit corrects a sensitivity difference between the pixels inside the pixel unit based on a correction coefficient. The present disclosure is applicable to, for example, a solid-state imaging device and the like.

A portable, rechargeable lateral flow test strip immunoassay in vitro diagnostic device includes a color imaging sensor for imaging a lateral flow test strip, a lens that images a substantial part of the 2D surface of the lateral flow test strip onto the color imaging sensor and image processing software that identifies patterns in the test lines and/or dots or other distributions in the test strip and compensates for distortions, non-uniformities or anomalies in the lines and/or dots or other distributions in the test strip.

The present disclosure illustrates a chromatic aberration adjusting method and apparatus of a detection device. The method includes the following steps: recording optical parameters corresponding to multiple image detection components when the multiple image detection components are respectively used to obtain images of multiple different standard films, selecting one of the optical parameters for each standard film, enter the selected optical parameter into a database as a base, and adjust the optical parameters of other image sensing components according to the database. The apparatus comprises a recording module, a selection module, an input module and an adjustment module.

A model training method includes: constructing a mapping model between original color information values and standard color information values of images with a coefficient matrix; establishing an objective function of the mapping model based on a regular term of a variable exponent; determining original color information values and standard color information values of a plurality of sample images; and calculating the objective function from the original color information values and the standard color information values of the plurality of sample images to determine the coefficient matrix.

The present disclosure provides a method and apparatus for generating a full-color holographic image. The method of generating a full-color holographic image includes forming images for each color channel based on complex hologram data extracted from rays propagating from a target object, and combining the formed images into one color image, wherein the images for each color channel are formed at reconstruction points for each color channel derived based on the complex hologram data.

An imaging apparatus is provided. The apparatus comprises a controller configured to transmit an image obtained by an image sensor to an external device via a communication circuit, and cause the external device to execute a predetermined image processing on the image. The controller obtains information indicating recommendation or non-recommendation for the execution of the predetermined image processing on the image by the external device, based on a state of at least one of an imaging optical system and the image sensor when capturing the image, and transmits an execution request for the predetermined image processing on the image to the external device via the communication circuit, if the obtained information indicates the recommendation.

Disclosed in the present invention are an image purple fringe eliminating system, method, a computer-readable storage medium, and a photographing device. The system traverses an image, calculates a hue of a pixel, counts a ratio of purple hue to adjacent purple hue pixels in the image, and calculates a dynamic purple fringe detection threshold; creates a mask with the same size as the image acquired by an image acquisition module, and detects the pixels that fall into a purple fringe area; corrects the detected pixel falling into the purple fringe area, correcting both a red channel intensity value and a blue channel intensity value of the pixels within the purple fringe region using a green channel intensity value. The invention can automatically correct the purple fringe of the image, improve the image shooting quality and the user experience.

A method of calibrating an image sensor includes obtaining a plurality of multi-light source images generated from a plurality of image sensor modules, wherein each of the plurality of image sensor modules generates at least three multi-light source images; obtaining, based on the plurality of multi-light source images, a plurality of crosstalk levels and a plurality of color-specific correction coefficients; generating modeling data based on a relationship between a crosstalk level for a first color and the plurality of color-specific correction coefficients; and obtaining, based on a single light source image captured by a first image sensor module and the modeling data, a color-specific correction coefficient for a target pixel of an image sensor, the image sensor being provided in the first image sensor module, and the color-specific correction coefficient being usable for correcting a crosstalk of the target pixel.

Systems and methods for automated, non-supervised, parameter-free segmentation of single cells and other objects in images generated by fluorescence microscopy. The systems and methods relate to both improving is image quality and to improved automatic segmentation on images. The methods will typically be performed on a digital image by a computer or processor running appropriate software stored in a memory.