Dynamic thin film interferometry is a technique used to non-invasively characterize the thickness of thin liquid films that are evolving in both space and time. Recovering the underlying thickness from the captured interferograms, unconditionally and automatically is still an open problem. A compact setup is provided employing a snapshot hyperspectral camera and the related algorithms for the automated determination of thickness profiles of dynamic thin liquid films. The technique is shown to recover film thickness profiles to within 100 nm of accuracy as compared to those profiles reconstructed through the manual color matching process. Characteristics and advantages of hyperspectral interferometry are discussed including the increased robustness against imaging noise as well as the ability to perform thickness reconstruction without considering the absolute light intensity information.

Dynamic thin film interferometry is a technique used to non-invasively characterize the thickness of thin liquid films that are evolving in both space and time. Recovering the underlying thickness from the captured interferograms, unconditionally and automatically is still an open problem. A compact setup is provided employing a snapshot hyperspectral camera and the related algorithms for the automated determination of thickness profiles of dynamic thin liquid films. The technique is shown to recover film thickness profiles to within 100 nm of accuracy as compared to those profiles reconstructed through the manual color matching process. Characteristics and advantages of hyperspectral interferometry are discussed including the increased robustness against imaging noise as well as the ability to perform thickness reconstruction without considering the absolute light intensity information.

A method of image processing based on a plurality of frames of images, an electronic device, and a storage medium are provided. The method includes: capturing a plurality of frames of original images; obtaining a high dynamic range (HDR) image by performing image synthesis on the plurality of frames of original images; performing artificial intelligent-based denoising on the HDR image to obtain a target denoised image.

A training method for an image denoising model that can include collecting multiple sample image groups through a shooting device, each sample image group including multiple frames of sample images with a same photographic sensitivity and sample images in different sample image groups having different photographic sensitivities. The method can further include acquiring a photographic sensitivity of each sample image group, determining a noise characterization image corresponding to each sample image group based on the photographic sensitivity, determining a training input image group and a target image associated with each sample image group, each training input image group including all or part of sample images in a corresponding sample image group and a corresponding noise characterization image, constructing multiple training pairs each including a training input image group and a target image, and training the image denoising model based on the multiple training pairs until the image denoising model converges.

A training method for an image denoising model that can include collecting multiple sample image groups through a shooting device, each sample image group including multiple frames of sample images with a same photographic sensitivity and sample images in different sample image groups having different photographic sensitivities. The method can further include acquiring a photographic sensitivity of each sample image group, determining a noise characterization image corresponding to each sample image group based on the photographic sensitivity, determining a training input image group and a target image associated with each sample image group, each training input image group including all or part of sample images in a corresponding sample image group and a corresponding noise characterization image, constructing multiple training pairs each including a training input image group and a target image, and training the image denoising model based on the multiple training pairs until the image denoising model converges.

An image synthesis unit of an X-ray imaging apparatus is configured to correct a synthesis target image or a transparent image based on movement information of a feature point and movement information of a pixel and generate a synthesized image by synthesizing a corrected synthesis target image and a transparent image or synthesizing a synthesis target image and a corrected transparent image.

An image synthesis unit of an X-ray imaging apparatus is configured to correct a synthesis target image or a transparent image based on movement information of a feature point and movement information of a pixel and generate a synthesized image by synthesizing a corrected synthesis target image and a transparent image or synthesizing a synthesis target image and a corrected transparent image.

An image processing apparatus includes a processor configured to extract a component related to luminance of each of a sample image and a processing target image that is to undergo image processing to match an impression of the processing target image to the sample image, extract feature values of the processing target image and the sample image by attaching to a pixel value of each pixel forming the processing target image and the sample image a weight responsive to the component related to the luminance, and make adjustment to match the feature value of the processing target image to the feature value of the sample image.

An image processing apparatus includes a processor configured to extract a component related to luminance of each of a sample image and a processing target image that is to undergo image processing to match an impression of the processing target image to the sample image, extract feature values of the processing target image and the sample image by attaching to a pixel value of each pixel forming the processing target image and the sample image a weight responsive to the component related to the luminance, and make adjustment to match the feature value of the processing target image to the feature value of the sample image.

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.