A snapshot hyperspectral imaging method includes the steps of: S1, selecting a set of reference wavelengths for calibration, rectifying the shifted positions due to dispersion at each reference wavelength, and selecting a center wavelength; S2, estimating relative dispersion at each reconstructed wavelength with respect to the center wavelength; S3, generating a dispersion matrix describing the direction of dispersion, and generating a spectral response matrix using a spectral response curve of a sensor; S4, capturing images blurred with dispersion; S5, deblurring the dispersed images captured in S4 using the dispersion matrix and the spectral response matrix generated in S3 to obtain spectral data spatially aligned in all spectrums; and S6, projecting the aligned spectral data obtained in S5 into color space, extracting a foreground image by a threshold method, sampling the dispersed images obtained in S4 as strong prior constraints for the foreground image, and reconstructing accurate spatial hyperspectral data.

Biosensor including an array of reaction sites and corresponding light sensors may experience crosstalk in which photons from one reaction site are detected by neighbors of its corresponding light sensor, and such crosstalk may be corrected using sharpening kernels corresponding to the sensors in the array. Such sharpening kernels may be derived from point spread functions, which may be determined in real time analysis based on images captured during sequencing.

A method includes obtaining multiple images of a scene using at least one red-green-blue-white (RGBW) image sensor. The method also includes generating multi-channel frames at different exposure levels from the images. The method further includes estimating motion across exposure differences between the different exposure levels using a white channel of the multi-channel frames as a guidance signal to generate multiple motion maps. The method also includes estimating saturation across the exposure differences between the different exposure levels to generate multiple saturation maps. The method further includes using the generated motion maps and saturation maps to recover saturations from the different exposure levels and generate a saturation-free RGBW frame. In addition, the method includes processing the saturation-free RGBW frame to generate a final image of the scene.

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.

An image processing method performed by an image processing device may include receiving input image data from an image sensor, selecting a convolution filter corresponding to each unit region from among a plurality of convolution filters based on color pattern information of the input image data, and generating, based on the selected convolution filter, a first image to be displayed from the input image data.

An electronic device includes a UDC and a UDC controller configured to determine an optimal number of frames required to be captured for a scene to compensate at least one parameter to optimize an output media using the UDC, obtain a multi-frame fusion media by performing at least one multi-frame fusion on the determined optimal number of frames, perform a light source spread correction on the multi-frame fusion media, and optimize the output media based on the light source spread correction on the multi-frame fusion media.

The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. Accurate intensity mappings facilitate accurate and robust normalization of sets of multiple digital images which, in turn, facilitates many additional types of operations carried out on sets of multiple normalized digital images, including change detection, quantitative enhancement, synthetic enhancement, and additional types of digital-image processing, including processing to remove artifacts and noise from digital images.

Described herein are methods and a system for pixel-by-pixel correction of diffuse reflected and specular reflected light on a display device of an information handling system. Voltage values of pixels of the display device are measured by sensors. Measured voltage value of a pixel are converted to a digital value. A digital correction value calculated based on whether diffuse reflected light or diffuse reflected light+specular reflected light is found on the pixel. A sigmoid transfer function and discrete sharpening filter is performed on the pixel, if the digital correction value is less than zero.

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.

A digital graduated filter is implemented in an imaging device by combining multiple images of the subject wherein the combining may include combining different numbers of images for highlights and for shadows of the subject. The imaging device may present a user with a set of pre-defined graduated filter configurations to choose from. A user may also specify the direction of graduation and strength of graduation in a viewfinder. In an alternative implementation, combining may include scaling of pixels being added instead of varying the number of images being combined. In an alternative implementation, the combining of multiple images may include combining a different number of images for highlights of the subject than for shadows of subject.