A method with image processing includes: setting an offset window for an offset pattern of a kernel offset and an offset parameter for an application intensity of the kernel offset; determining an output kernel by applying the kernel offset to an input kernel based on the offset window and the offset parameter; and adjusting contrast of a degraded image using the output kernel.

A computer-implemented method is provided for improving live video quality. The method comprises: (a) acquiring, using a medical imaging apparatus, a stream of consecutive image frames of a subject; (b) feeding the stream of consecutive image frames to a first set of denoising components, wherein each of the first set of denoising components is configured to denoise an image frame from the stream of consecutive image frames in a spatial domain to output an intermediate image frame; (c) feeding a plurality of the intermediate image frames to a second denoising component, wherein the second denoising component is configured to (i) denoise the plurality of the intermediate image frames in a temporal domain and (ii) generate a weight map; and outputting a final image frame with improved quality in both temporal domain and spatial domain based at least in part on the weight map.

Audio distortion compensation methods to improve accuracy and efficiency of audio content identification are described. The method is also applicable to speech recognition. Methods to detect the interference from speakers and sources, and distortion to audio from environment and devices, are discussed. Additional methods to detect distortion to the content after performing search and correlation are illustrated. The causes of actual distortion at each client are measured and registered and learnt to generate rules for determining likely distortion and interference sources. The learnt rules are applied at the client, and likely distortions that are detected are compensated or heavily distorted sections are ignored at audio level or signature and feature level based on compute resources available. Further methods to subtract the likely distortions in the query at both audio level and after processing at signature and feature level are described.

For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

A device, system, and/or method may be used to provide an adaptive alignment. A first video data may be received. The first video data may comprise a first video frame and a second video frame. Sensor pose data may be determined. The pose data may be associated with the first video frame and the second video frame. An adjusted video frame may be determined based on the first video frame and a motion indicated by the pose data. A frame adjustment value may be determined by comparing a first pixel from the adjusted video frame to a second pixel from the second video frame. The frame adjustment value may correlate the pose data to the first video data. A second video data may be determined by applying the frame adjustment value.

Audio distortion compensation methods to improve accuracy and efficiency of audio content identification are described. The method is also applicable to speech recognition. Methods to detect the interference from speakers and sources, and distortion to audio from environment and devices, are discussed. Additional methods to detect distortion to the content after performing search and correlation are illustrated. The causes of actual distortion at each client are measured and registered and learnt to generate rules for determining likely distortion and interference sources. The learnt rules are applied at the client, and likely distortions that are detected are compensated or heavily distorted sections are ignored at audio level or signature and feature level based on compute resources available. Further methods to subtract the likely distortions in the query at both audio level and after processing at signature and feature level are described.

Methods, apparatuses and systems may provide for applying an image to a filter, wherein the filter includes data-adaptive weights. Additionally, an output scaling factor may be determined based on one or more statistical operators applied to the data-adaptive weights and the output scaling factor may be applied to an output of the filter.

An image processing device including: one or more processors comprising hardware, wherein the one or more processors are configured to: calculate an estimated movement amount of a subject in each image of a plurality of images; perform, based on the estimated movement amounts, one of: select and output an image that is most recently captured among the plurality of images; and select a reference image from the plurality of images based on the estimated movement amounts of the subject in the plurality of images; in response to selecting the reference image, determine a gain of the reference image; and perform, based on the gain, one of: select and output the reference image; and a synthesis process including: select a synthesis target image from the plurality of images; and generate and output a synthesized image by synthesizing the synthesis target image and the reference image.

An analysis device includes a controller. The analysis device receives moving images sent from a plurality of imaging devices, analyzes the received moving images, and sends an analysis result of a moving image to, among a plurality of display devices, a display device which has made a request to display so that the display device displays the analysis result. The controller controls a processing order of reception processes and analysis processes on moving images sent from the imaging devices according to degrees of priority preset for the respective imaging devices.

The present disclosure generally relates to the field of camera surveillance, and in particular to a method and control unit for controlling a bitrate of a video stream captured with an image acquisition device.