A system for a target image reconstruction includes a stepped frequency transmitter configured to emit a stepped frequency waveform having different constant frequencies at different periods of time and a modulator configured to modulate the stepped frequency waveform emitted at each period of time with a modulation signal to output a modulated stepped frequency waveform with an increased bandwidth. The system includes a transceiver configured to transmit the modulated stepped frequency waveform to a target and to accept reflection of the modulated stepped frequency waveform reflected from the target, a mixer to interfere the unmodulated stepped frequency waveform and the reflection of the modulated stepped frequency waveform to produce a beat signal of the interference of the unmodulated stepped frequency waveform with the reflection of the modulated stepped frequency waveform, and a signal processor to reconstruct an image of the target from the beat signal.

The disclosure is related to systems and methods for generating an animation with a perception of real depth from one or more images. In some embodiments, a computing server may receive an input image. The input image captures a scene. The computing server may generate a set of layers of images, which may include one or more processed images. A processed image may be a version of the input image. The computing server may generate one or more variable masks that are used for a superposition of the set of layers. The variable masks may include a plurality of regions specifying different compositions of the layers in the superposition. The computing server may generate an animation of the scene from the set of layers. The animation may include a composite frame generated from the set of layers of images superimposed according to the variable masks.

A method, apparatus, and computer-readable medium for point cloud coefficient coding are provided. Transform coefficients associated with point cloud data are decomposed into set-index values and symbol-index values, the symbol index-value specifying location of the transform coefficient within a set. The decomposed transform coefficients are partitioned into one or more sets based on the set-index values and the symbol-index values. The set-index values of the partitioned transform coefficients are entropy-coded, and the symbol-index values of the partitioned transform coefficients are bypass-coded. The point cloud data is compressed based on the entropy-coded symbol-index values and the bypass-coded set-index values.

The disclosure relates to method and system for image scaling. The method includes determining a nature of image scaling required to be performed on an input image based on a vertical scaling ratio and a horizontal scaling ratio and includes determining if the image scaling is an upscaling or a downscaling, a symmetric scaling or an asymmetric scaling. The method further includes determining an overall scaling ratio based on a lower or an equal of the vertical scaling ratio and the horizontal scaling ratio. The method further includes scaling an input image to a target image using a polyphase finite impulse response (FIR) scaling filter based on the nature of the image scaling, the overall scaling ratio, and a structure of the polyphase FIR scaling filter. The scaling includes dynamically performing at least one of duplication of lines, addition of filler lines, duplication of pixels, and addition of filler pixels.

A rectangle creating unit creates a rectangle circumscribing a lesion area in a medical image. A division-number-ratio calculating unit calculates a division-number ratio based on an image aspect ratio of an input image to be input to a device that identifies a lesion and on a rectangle aspect ratio between the length in the vertical direction of and the length in the horizontal direction of the rectangle. A multiplying-factor calculating unit calculates, based on the division-number ratio, a resizing multiplying-factor for each of the vertical direction and the horizontal direction of a rectangular image encircled by the rectangle and including the lesion area. A resizing unit resizes the rectangular image with the resizing multiplying-factor. A dividing unit divides the resized rectangular image into one or more images in such a manner that the size of each divided image matches the size of the input image.

Methods, systems, and articles of manufacture, including computer program products, are provided for image cleanup. In some embodiments, there is provide a method which may include subsampling a first image to a first level image of a multiscale transform; performing, based on a machine learning model, an identification of a foreground portion of the first level image and a background portion of the first level image; generating, based on the identification of the foreground portion and the background portion, a first mask; upscaling the first mask to a resolution corresponding to the first image depicting the foreground item; applying the upscaled first mask to the first image to form a second image depicting the foreground item; and providing the second image depicting the foreground item to a publication system. Related systems and articles of manufacture, including computer program products, are also provided.

A camera system captures an image in a source aspect ratio and applies a transformation to the input image to scale and warp the input image to generate an output image having a target aspect ratio different than the source aspect ratio. The output image has the same field of view as the input image, maintains image resolution, and limits distortion to levels that do not substantially affect the viewing experience. In one embodiment, the output image is non-linearly warped relative to the input image such that a distortion in the output image relative to the input image is greater in a corner region of the output image than a center region of the output image.

A framework for automatically finding an organ in image data. In accordance with one aspect, a predetermined view of the organ is approximated by transforming the original image data to generate transformed image data. A best-match region in the transformed image data that best matches a synthesized geometric shape may then be found. The best-match region may be transformed into a volume space of the original image data to generate a location of the organ.

An image processing method includes: grouping input image data of a dilation convolution to obtain D.sub.h×D.sub.w grouped image data; wherein D.sub.h is a dilation rate of a convolution kernel corresponding to the dilation convolution on a height dimension thereof, D.sub.w is a dilation rate of a convolution kernel corresponding to the dilation convolution on a width dimension thereof, and both D.sub.h and D.sub.w are positive integers; performing a convolution calculation on the D.sub.h×D.sub.w grouped image data respectively by a first convolution kernel, to obtain D.sub.h×D.sub.w grouped convolution calculation results; wherein the first convolution kernel is a convolution kernel before the dilation convolution is dilated; and obtaining a dilation convolution calculation result of the input image data according to the D.sub.h×D.sub.w grouped convolution calculation results. The present disclosure can reduce power consumption, improve efficiency of the dilation convolution calculation.

Systems and methods upscale an input image by a final upscaling factor. The systems and methods employ a first module implementing a super resolution neural network with feature extraction layers and multiple sets of upscaling layers sharing the feature extraction layers. The multiple sets of upscaling layers upscale the input image according to different respective upscaling factors to produce respective first module outputs. The systems and methods select the first module output with the respective upscaling factor closest to the final upscaling factor. If the respective upscaling factor for the selected first module output is equal to the final upscaling factor, the systems and methods output the selected first module output. Otherwise, the systems and methods provide the selected first module output to a second module that upscales the selected first module output to produce a second module output corresponding to the input image upscaled by the final upscaling factor.