Distortion generated in an image is effectively corrected in imaging using an EPI sequence such as DWI without extending an imaging time. After one excitation RF pulse of EPI is applied, a navigator scan in which the polarity of the phase encoding is opposite to that of the main scan is performed continuously to the main scan, and the distortion of the image by using the navigator scan data obtained by the navigator scan is corrected. In a case of multi-shot, phase information obtained from the navigator scan data for each shot is used to perform phase correction and multi-shot reconstruction on the main scan data of each shot.

Systems and methods are provided for the denoising of images in the presence of broadband noise based on the detection and/or estimation of in-band noise. According to various example embodiments, an estimate of broadband noise that lies within the imaging band is made by detecting or characterizing the out-of-band noise that lies outside of the imaging band. This estimated in-band noise may be employed for denoise the detected imaging waveform. According to other example embodiments, a reference receive circuit that is sensitive to noise within the imaging band, but is isolated from the imaging energy, may be employed to detect and/or characterize the noise within the imaging band. The estimated reference noise may be employed to denoise the detected in-band imaging waveform.

Systems and methods are provided for the denoising of images in the presence of broadband noise based on the detection and/or estimation of in-band noise. According to various example embodiments, an estimate of broadband noise that lies within the imaging band is made by detecting or characterizing the out-of-band noise that lies outside of the imaging band. This estimated in-band noise may be employed for denoise the detected imaging waveform. According to other example embodiments, a reference receive circuit that is sensitive to noise within the imaging band, but is isolated from the imaging energy, may be employed to detect and/or characterize the noise within the imaging band. The estimated reference noise may be employed to denoise the detected in-band imaging waveform.

Temporal filtering operations may be reset for certain pixels within an image frame to reduce contribution from previous input frames to reduce ghosting and other artifacts. The resetting reduces the contribution to, for example, zero, either immediately or within a predetermined period of time (e.g., a certain number of frames). A decision regarding whether to reset temporal filtering for a pixel of the image frame may be based on a probability assigned to that pixel. The probability can be based on rules with one or more criteria. One example factor for adjusting probability is a confidence level regarding the temporal filtering decision for the pixel, in which the probability for a random reset of a pixel is based on the confidence level regarding the temporal filtering decision for those pixels.

Temporal filtering operations may be reset for certain pixels within an image frame to reduce contribution from previous input frames to reduce ghosting and other artifacts. The resetting reduces the contribution to, for example, zero, either immediately or within a predetermined period of time (e.g., a certain number of frames). A decision regarding whether to reset temporal filtering for a pixel of the image frame may be based on a probability assigned to that pixel. The probability can be based on rules with one or more criteria. One example factor for adjusting probability is a confidence level regarding the temporal filtering decision for the pixel, in which the probability for a random reset of a pixel is based on the confidence level regarding the temporal filtering decision for those pixels.

A processor detects a structure of interest from a plurality of tomographic images indicating a plurality of tomographic planes of an object. The processor selects a tomographic image from the plurality of tomographic images according to a type of the structure of interest in a region in which the structure of interest has been detected and generates a composite two-dimensional image using the selected tomographic image in the region in which the structure of interest has been detected and using a predetermined tomographic image in a region in which the structure of interest has not been detected.

A processor detects a structure of interest from a plurality of tomographic images indicating a plurality of tomographic planes of an object. The processor selects a tomographic image from the plurality of tomographic images according to a type of the structure of interest in a region in which the structure of interest has been detected and generates a composite two-dimensional image using the selected tomographic image in the region in which the structure of interest has been detected and using a predetermined tomographic image in a region in which the structure of interest has not been detected.

A system includes a processing unit communicatively coupled to a detector array of an optical wafer characterization system. The processing unit is configured to perform one or more steps of a method or process including the steps of acquiring one or more target images of a target location on a wafer from the detector array, applying a de-noising filter to at least the one or more target images, determining one or more difference images from one or more reference images and the one or more target images, and up-sampling the one or more difference images to generate one or more up-sampled images. One or more wafer defects are detectable in the one or more difference images or the up-sampled images.

A time sequenced series of optical images of a patient is obtained at a rate faster than cardiac frequency, wherein the time sequenced series of images capture one or more physical properties of intrinsic contrast. A cross-correland signal from the patient is obtained. A cross-correlated wavelet transform analysis is applied to the time sequenced series of optical images to yield a spatiotemporal representation of cardiac frequency phenomena. The cross-correlated wavelet transform analysis comprises performing a wavelet transform on the time-sequenced series of optical images to obtain a wavelet transformed signal, cross-correlating the wavelet transformed signal with the cross-correland signal to obtain a cross-correlated signal, filtering the cross-correlated signal at cardiac frequency to obtain a filtered signal, and performing an inverse wavelet transform on the filtered signal to obtain a spatiotemporal representation of the time sequenced series of optical images. Images of the cardiac frequency phenomena are generated.

A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.