A method for evaluating images of a printed pattern. The method includes obtaining a first averaged image of the printed pattern, where the first averaged image is generated by averaging raw images of the printed pattern. The method also includes identifying one or more features of the first averaged image. The method further includes evaluating the first averaged image, using an image quality classification model and based at least on the one or more features. The evaluating includes determining, by the image quality classification model, whether the first averaged image satisfies a metric.

In some embodiments, a method for processing inspection data associated with cargo irradiated by a plurality N of pulses of inspection is provided. The method includes obtaining the inspection data, the inspection data being representative of intensity values of pixels of an inspection image of the including data associated with a higher energy mode, and data associated with a lower energy mode; generating a histogram having, as a first axis, bins corresponding to pixel intensity values HM associated with the higher energy mode and, as a second axis, bins corresponding to pixel intensity values LM associated with the lower energy mode; selecting a bin corresponding to a most frequent bin of the pixel intensity values HM; and generating a transformation table by mapping each bin of the pixel intensity values LM with the selected bin of the pixel intensity values HM.

A computer-implemented method is provided for improving image quality with shortened acquisition time. The method comprises: determining an accelerated image acquisition parameter for imaging a subject using a medical imaging apparatus; acquiring, using the medical imaging apparatus, a medical image of the subject according to the accelerated image acquisition parameter; applying a deep network model to the medical image to generate a corresponding transformed medical image with improved quality; and combining the medical image and the corresponding transformed medial image using an adaptive mixing algorithm to generate output image.

A computed tomography (CT) image processing apparatus and a CT image processing method are provided. The CT image processing apparatus may generate a virtual monochromatic image (VMI) by applying a weight to each of first, second, and third images corresponding to three different energy ranges. The CT image processing apparatus may set a region of interest (ROI) on a CT image, determine a VMI at an energy level at which a CNR of the ROI is at a maximum among a plurality of VMIs, and display the determined VMI.

Provided are an intraoral image processing method and an intraoral image processing apparatus. The intraoral image processing method according to an embodiment may include: obtaining a three-dimensional oral cavity model of an oral cavity; obtaining curvature information of the three-dimensional oral cavity model; obtaining roughness information of the three-dimensional oral cavity model, based on the curvature information; obtaining a color of the three-dimensional oral cavity model, based on the roughness information; and displaying the three-dimensional oral cavity model, based on the obtained color.

A method for generating a temporary image includes acquiring first data of an examination object, and providing at least one initialization image by applying a first processing function and/or a second processing function to the first data. The first processing function and the second processing function are at least partially different. The at least one initialization image is visualized. Further data of the examination object is acquired. Result data is provided by applying the first processing function to the further data. A result image is provided by applying the second processing function to the further data and/or the result data. The result data is provided before the result image. The temporary image is generated based on the result data and the at least one initialization image. The temporary image is visualized, and the result image is visualized.

Exemplary embodiments are directed to a system for selective replacement of an object in an image. The system includes an interface configured to receive as input an original image and a background image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more objects in the original image, generate a neural network mask of the original image for the one or more objects in the original image, generate a filtered original image including the original image without the one or more objects, generate a modulated background image including a replacement background based on the neural network mask, and generate a combined image including the filtered original image combined with the modulated background image.

An ultrasonic image is obtained from a bias-switchable row-column array transducer. A row channel data set is obtained by applying a bias voltage pattern to groups of row electrodes, the bias voltage pattern being chosen such that row electrodes within each group have the same bias voltage; transmitting a waveform along each of the plurality of row electrodes; and recording received column signals from each of the plurality of column electrodes. A column channel data set is obtained by applying a bias voltage pattern to groups of column electrodes, the bias voltage pattern being chosen such that column electrodes within each group have the same bias voltage; transmitting a waveform along each of the plurality of column electrodes; and recording received row signals from each of the plurality of row electrodes.

The present disclosure relates to a method and apparatus for generating training data of a deep learning model for lane classification. The method according to an embodiment of the present disclosure is performed by an electronic apparatus, and is a method for generating training data of a deep learning model for lane classification by generating a composite image of the other color lane using images of a white lane and the other color lane, and includes determining a ratio of other two channels based on one channel (reference color channel) for three color channels of red (R), green (G) and blue (B) of the other color lane in the image of the other color lane; and generating a composite image of the other color lane by scaling the image of the white lane by applying the determined ratio to the other two channels with respect to the reference color channel of the white lane.

The present specification relates to a method for enabling an operator to perform visual layer separation, the method including: retrieving at least one X-ray scan image from a memory in data communication with an inspection system, wherein the image comprises a first area of pixels representative of a target object obscured by a clutter object and a second area of pixels representative of the clutter object; receiving a selection of the pixels representative of the first area; receiving a selection of the pixels representative of the second area; determining if the selected second area meets a predefined quality threshold; if the selected second area meets the predefined quality threshold, generating a modified at least one X-ray image; and if the selected second area does not meet the predefined quality threshold, prompting the operator to select a different second area of pixels representative of the clutter object.