A method (400) of capturing and processing electroluminescence (EL) images (1910) of a PV array (40) is disclosed herein. In a described embodiment, the method 400 includes controlling the aerial vehicle (20) to fly along a flight path to capture EL images (1910) of corresponding PV array subsections (512b) of the PV array (40), deriving respective image quality parameters from at least some of the captured EL images, dynamically adjusting a flight speed of the aerial vehicle along the flight path, based on the respective image quality parameters for capturing the EL images (1910) of the PV array subsections (512b), extracting a plurality of frames (1500) of the PV array subsection (512b) from the EL images (1910); determining a reference frame having a highest image quality of the PV array subsection (512b) from among the extracted frames (2100); performing image alignment of the extracted frames (2100) to the reference frame to generate image aligned frames (2130), and processing the image aligned frames (2130) to produce an enhanced image (2140) of the PV array subsection (512b) having a higher resolution than the reference frame. A system, image processing device, and aerial vehicle for the method thereof are also disclosed.

A CPU acquires a distance image or a visible light image captured by a TOF camera or a visible light camera that has, as an imageable region, a region including an irradiation region which is a space in which a breast of a subject imaged by a mammography apparatus is irradiated with radiation emitted from a radiation source and detects whether or not a foreign object other than an object to be imaged is present in the irradiation region on the basis of the distance image or the visible light image.

Devices, systems, and methods are provided for testing and validation of a camera. A device may capture a first image of a target using a camera, wherein the camera is in a clean state, and wherein the target is in a line of sight of the camera. The device may apply an obstruction to a portion of a lens of the camera. The device may apply a camera cleaning system to the lens of the camera. The device may capture a post-clean image after applying the camera cleaning system. The device may determine a post-clean SSIM score based on comparing the post clean image to the first image. The device may compare the post-clean SSIM score to a validation threshold. The device may determine a validation state of the camera cleaning system based on the comparison.

The present disclosure provides a system and method for motion field generation and image correction. The method may include obtaining a plurality of first sets of magnetic resonance (MR) image data of an object generated based on a plurality of first sets of imaging sequences. The method may include obtaining a motion curve of the object. The method may include obtaining position emission tomography (PET) image data of the object generated in a scanning time period. The method may include generating one or more target motion fields corresponding to the scanning time period based on the plurality of first sets of MR image data and the motion curve. The method may include generating one or more corrected PET images by correcting, based on the one or more target motion fields, the PET image data.

A method of producing information from at least one camera image of an object, including: A) recording raw image data of the at least one camera image, B) evaluating the raw image data by a mathematical linkage to produce combination image data, C) deriving the information from the combination image data, D) outputting the information, E) determining an actual measure for a data quality of the raw image data prior to or after evaluation steps in step B), F) determining a deviation between the actual measure for the data quality and a target measure for the data quality of the raw image data of at least one camera image, and G) again recording all raw image data of those camera images, for which the deviation determined in step F) is greater than a predetermined threshold value and repeating at least one evaluation step from step B) and steps C) to F) either until the deviation determined in step F) for the raw image data of all camera images from the plurality of camera images is less than the threshold value or until a predetermined termination condition is fulfilled.

A deposit detection device according to an embodiment includes an adhesion detection module, a moving adhesion detection module, and a determination module. The adhesion detection module detects a deposit region corresponding to a deposit adhering to an imaging device, based on brightness information of an image captured by the imaging device. The moving adhesion detection module detects the deposit region detected during moving of the vehicle as a moving deposit region, from among the deposit regions detected by the adhesion detection module. When the area of the moving deposit region detected by the moving adhesion detection module is equal to or larger than a first threshold value, the determination module determines that there is a deposit.

The technology disclosed relates to classification of process cycle images to predict success or failure of process cycles. The technology disclosed includes capturing and processing images of sections arranged on an image generating chip in genotyping process. Image description features of production cycle images are created and given as input to classifiers. A trained classifier separates successful production images from unsuccessful or failed production images. The failed production images are further classified by a trained root cause classifier into various categories of failure.

Devices, methods, and computer-readable media are disclosed, describing an adaptive, subject-aware approach for image bracket selection and fusion, e.g., to generate high quality images in a wide variety of capturing conditions, including low light conditions. An incoming image stream may be obtained from an image capture device, comprising images captured using differing default exposure values, e.g., according to a predetermined pattern. When a capture request is received, it may be detected whether one or more human or animal subjects are present in the incoming image stream. If a subject is detected, an exposure time of one or more images selected from the incoming image stream may be reduced relative to its default exposure time. Prior to the fusion operation, one of the selected images may be designated a reference image for the fusion operation based, at least in part, on a sharpness score and/or a blink score of the image.

Systems and methods for generating and using statistical models to mitigate atmospheric effects in images are described. In some embodiments, a statistical model may be generated by selecting a vegetation type that grows in continuous healthy canopies; identifying a vegetation reference value that is a stable reflectance property of the vegetation type; in a plurality of images, selecting one or more plots of the vegetation type and obtaining top-of-atmosphere reflectance for the plots; selecting discrete areas near the plots and obtaining top-of-atmosphere reflectance for the discrete areas; obtaining image statistics for the discrete areas; and generating a statistical model based on the acquired data.

An electronic device and an operating method of the electronic device are provided. The electronic device includes: a communication interface comprising communication circuitry, a memory storing one or more instructions, and a processor, when executing the one or more instructions stored in the memory, is configured to: while content is executed, identify an execution condition of the content, select a frame-processing mode between an image quality-preference mode and an input lag-preference mode, based on the identifying of the execution condition, the image quality-preference mode being a frame-processing mode in which processing is performed by taking into account image quality of the content preferentially to an input lag of the content, and the input lag-preference mode being a frame-processing mode in which processing is performed by taking into account the input lag of the content preferentially to the image quality of the content, and perform image-quality processing on frames of the content based on the selected frame-processing mode.