A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The system includes a part inspection module communicatively coupled to the vision device and receives the digital image of the part as an input image. The part inspection module includes a defect detection model. The defect detection model includes a template image. The defect detection model compares the input image to the template image to identify defects. The defect detection model generates an output image. The defect detection model configured to overlay defect identifiers on the output image at the identified defect locations, if any.

Aspects of the disclosure provide a device for processing frames with aliasing artifacts. For example, the device can include a motion estimation circuit, a warping circuit coupled to the motion estimation circuit, and a temporal decision circuit coupled to the warping circuit. The motion estimation circuit can estimate a motion value between a current frame and a previous frame. The warping circuit can warp the previous frame based on the motion value such that the warped previous frame is aligned with the current frame and determine whether the current frame and the warped previous frame are consistent. The temporal decision circuit can generate an output frame, the output frame including either the current frame and the warped previous frame when the current frame and the warped previous frame are consistent, or the current frame when the current frame and the warped previous frame are not consistent.

Embodiments described herein provide a system for assessing the surface of an object for detecting contamination or other defects. During operation, the system obtains an input image indicating the contamination on the object and generates a synthetic image using an artificial intelligence (AI) model based on the input image. The synthetic image can indicate the object without the contamination. The system then determines a difference between the input image and the synthetic image to identify an image area corresponding to the contamination. Subsequently, the system generates a contamination map of the contamination by highlighting the image area based on one or more image enhancement operations.

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 system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

There is provided an optical navigation device including an image sensor and a processing unit. The image sensor outputs successive image frames. The processing unit calculates a contamination level and a motion signal based on filtered image frames, and determines whether to update a fixed pattern noise (FPN) stored in a frame buffer according to a level of FPN subtraction, the calculated contamination level and the calculated motion signal to optimize the update of the fixed pattern noise.

A method includes: computing noise data by subtracting, by a processing circuit, a noisy image from a corresponding ground truth image; clustering, by the processing circuit, a plurality of noise values of the noise data based on intensity values of the corresponding ground truth image; permuting, by the processing circuit, a plurality of locations of the noise values of the noise data within each cluster; generating, by the processing circuit, a synthetic noise image based on the permuted locations of the noise values; adding, by the processing circuit, the synthetic noise image to the corresponding ground truth image to generate a synthetic noisy image; and augmenting an image dataset for training a neural network to perform image denoising with the synthetic noisy image.

Irradiation light in a visible light region is irradiated to a sample while switching irradiation of infrared light IR having a wavelength that corresponds to the infrared absorption spectrum of an observation target material included in the sample between a first state and a second state. A first image and a second image are generated based on the phase distribution, the intensity distribution, and the polarization direction distribution of the light including the irradiation light that has passed through the sample in synchronization with the switching of the infrared light IR irradiation between the first state and the second state. Subsequently, an output image is generated so as to represent one from among the position, size, and shape based on the difference and/or ratio with respect to the pixel values for each pixel between the first image and the second image.

A method of enhancing an x-ray image is disclosed. The method involves obtaining an input image based on a source x-ray image of an object. Compositional information representing physical characteristics of the object is also obtained. An image enhancement process is applied to the input image to generate a processed image. Application of the image enhancement process is controlled by one or more parameters determined in dependence on the compositional information. An output image is then provided based on the processed image.