When performing noise removal of an image signal by applying the background art, there is a case where a filter coefficient that causes an edge constituting the image to become blurred against expectation due to the influence of noise in the image signal is set. The methods to solve this problem include a method for calculating a wide range of image feature values needed for correction of the filter coefficient, but this method has the problem that it requires a significant amount of calculation, thus increasing the calculation cost. To solve the above problem, the present invention is provided with an image signal input means, a wavelet transformation means, a first structure/texture separating means, a texture component degenerating means, a first combining means, an inverse wavelet transformation means, and a second structure/texture separating means. The present invention is further provided with a second texture component degenerating means, a second combining means, and an image signal output means.

System and method for detecting the authenticity of products by detecting a unique chaotic signature. Photos of the products are taken at the plant and stored in a database/server. The server processes the images to detect for each authentic product a unique authentic signature which is the result of a manufacturing process, a process of nature etc. To detect whether the product is genuine or not at the store, the user/buyer may take a picture of the product and send it to the server (e.g. using an app installed on a portable device or the like). Upon receipt of the photo, the server may process the receive image in search for a pre-detected and/or pre-stored chaotic signature associated with an authentic product. The server may return a response to the user indicating the result of the search. A feedback mechanism may be included to guide the user to take a picture at a specific location of the product where the chaotic signature may exist.

A method for correction of an x-ray image recorded with an x-ray device with an anti-scatter grid for effects of the anti-scatter grid is provided. The anti-scatter grid has a spatially periodically repeating geometrical embodiment, and a calibration image recorded without an imaging object is used. The calibration image and the x-ray image are transformed by a transformation into the position frequency space. In the position frequency space, adaptation parameters describing changes of the calibration image optimizing a measure of matching between the x-ray image and the calibration image are established. For correction, the adapted calibration image is subtracted from the x-ray image, and the x-ray image is transformed back into the position space again using an inverse of the transformation.

Methods and systems for image processing are provided. A target image may be acquired, wherein the target image may include a plurality of elements, an element of which may correspond to a pixel or a voxel. The target image may be decomposed into at least one layer, wherein the at least one layer may include a low frequency sub-image and a high frequency sub-image. The at least one layer may be transformed. The transformed layer may be reconstructed into a composite image.

Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself—rather than using a priori definitions for what should constitute a measurable unit of action—identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

Systems, apparatuses, and methods for diagnosing ureteropelvic junction obstruction. A set of biomarkers may be extracted from each of one or more time-activity curves associated with diuresis renography and/or functional magnetic resonance urography of one or more kidneys of a patient. One or more calculations can be performed based on the set of biomarkers to identify uretero-pelvic junction obstruction and a classification of severity or criticality thereof.

Embodiments of the disclosure provide an image amplifying method, an image amplifying device, and a display apparatus, and relate to field of image processing technique, the method comprises: obtaining, by an image amplifying device, high-frequency and low-frequency components of a source image; performing, by the image amplifying device, pixel interpolation on the low-frequency components of the source image through a first interpolation algorithm, to obtain a low-frequency sub-image; performing, by the image amplifying device, pixel interpolation on the high-frequency components of the source image through a second interpolation algorithm, to obtain a high-frequency sub-image; and merging, by the image amplifying device, the low-frequency and high-frequency sub-images, to obtain a merged image; wherein the first interpolation algorithm and the second interpolation algorithm adopt different algorithms, so that it can ensure image quality of the amplified image while reducing the operation amount. Embodiments of the disclosure are applied to image amplification.

What is disclosed is a system and method for determining when a subject is speaking from a respiratory signal obtained from a video of that subject. A video of a subject is received and a respiratory signal is extracted from a time-series signal is obtained from processing pixels in image frames of the video. The respiratory signal comprises an inspiratory signal and an expiratory signal. Cycle-level feature are extracted from the respiratory signal and used to identify expiratory signals during which speech is likely to have occurred. The identified expiratory signal are divided into time intervals. Frame-level features are determined for each time interval and an amount of distortion in the expiratory signal for this time interval is quantified. The amount of distortion is compared to a threshold. In response to the comparison, a determination is made that speech occurred during this interval. The process repeats for all time intervals.

A first image including a first sensor placed on a target can be recorded at a first resolution. Using an accelerometer, an orientation of the first image can be determined concurrently with recording of the first image. The orientation of the first image can be associated with an orientation of the first sensor placed on the target within the first image. A user input indicating a boundary surrounding the first sensor in a portion of the first image can be received via a graphical user interface (GUI). The orientation of the first sensor within the first image can be compared with a predetermined reference orientation and a determination made whether the orientation of the first sensor within the first image matches the predetermined reference orientation. When the orientation of the first sensor within the first image matches the predetermined reference orientation, a confirmation can be output via the GUI.

A WT unit performs wavelet transformation on original image data to generate first image data. An ROI developing unit, based on ROI information, specifies an ROI corresponding portion corresponding to an ROI and a non-ROI corresponding portion corresponding to a non-ROI to the first image data. A high-frequency cutting unit performs, on the first image data, a high-frequency cutting process that cuts a high-frequency component of the non-ROI corresponding portion. A low-frequency blurring unit performs, on the first image data, a low-frequency blurring process that blurs a low-frequency component of the non-ROI corresponding portion. The IWT unit performs inverse wavelet transformation on second image data (first image data obtained after the high-frequency cutting process and the low-frequency blurring process are performed) to generate third image data.