A method is described for converting an input image into an output image, the output image including an output luminance component made of elements. The method includes: obtaining an input luminance component from the input image; determining the output luminance component, the respective ranges of the output luminance component element values and input luminance component element values being of different range extension, the determining step including: —determining a first intermediate luminance component from the input luminance component and an exponent, —obtaining a mapping profile allowing for mapping a luminance component based on the input luminance component into the output luminance component, —determining a second intermediate luminance component from the input luminance component and the obtained mapping profile, —determining the output luminance component from the first and second intermediate luminance components; and converting the input image into the output image.

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.

A tiling unit assigning primitives to tiles in a graphics processing system which has a rendering space subdivided into a plurality of tiles. Each tile can comprise one or more polygonal region. Mesh logic of the tiling unit can determine that a plurality of primitives form a mesh and can determine whether the mesh entirely covers a region. If the mesh entirely covers the region then a depth threshold for the region can be updated so that subsequent primitives which lie behind the depth threshold are culled (i.e. not included in the display list for a tile). This helps to reduce the number of primitive IDs included in a display list for a tile which reduces the amount of memory used by the display list and reduces the number of primitives which a hidden surface removal (HSR) module needs to fetch to perform HSR on the tile.

In one embodiment, a method includes receiving a source image and its associated parameters from each of multiple image sources, associating each of the source images with a layer in a range of layers based on the parameters associated with the source images, the range of layers specifying a composition layering order of the source images, generating a corresponding customized distortion mesh for each particular source image in the source images based on the parameters associated with the particular source image and at least a portion of the parameters associated with each of the source images that is associated with any layer preceding a layer associated with the particular source image, modifying each of the source images using the corresponding customized distortion mesh, generating a composite image using the modified source images, and displaying the composite image as a frame in a video.

A method of reducing radiation dose for radiology imaging modalities and nuclear medicine by using a convolutional network to generate a standard-dose nuclear medicine image from low-dose nuclear medicine image, where the network includes N convolution neural network (CNN) stages, where each stage includes M convolution layers having K×K kernels, where the network further includes an encoder-decoder structure having symmetry concatenate connections between corresponding stages, downsampling using pooling and upsampling using bilinear interpolation between the stages, where the network extracts multi-scale and high-level features from the low-dose image to simulate a high-dose image, and adding concatenate connections to the low-dose image to preserve local information and resolution of the high-dose image, the high-dose image includes a dose reduction factor (DRF) equal to 1 of a radio tracer in a patient, the low-dose PET image includes a DRF of at least 4 of the radio tracer in the patient.

A method for compensating a depth-dependent attenuation in ultrasonic signal data of a medium and a system for performing the method. The method is implemented by a processing system (8), the method comprising the following: processing (c), in which ultrasound signal data is processed by the processing unit for providing in-phase and quadrature phase (IQ) data of the medium, and attenuation compensation (f), in which a phase of the IQ data is compensated as a function of a respective frequency shift amount for each of a plurality of different depths (z.sub.1, z.sub.2, z.sub.n) in the medium, such that the IQ data spectrum is recentered across the plurality of different depths.

In one embodiment, a method includes receiving a source image and its associated parameters from each of multiple image sources, associating each of the source images with a layer in a range of layers based on the parameters associated with the source images, the range of layers specifying a composition layering order of the source images, generating a corresponding customized distortion mesh for each particular source image in the source images based on the parameters associated with the particular source image and at least a portion of the parameters associated with each of the source images that is associated with any layer preceding a layer associated with the particular source image, modifying each of the source images using the corresponding customized distortion mesh, generate a composite image using the modified source images, and display the composite image as a frame in a video.

A computer-implemented method is provided for improving image quality with shortened acquisition time. The method comprises: determining an accelerated image acquisition scheme for imaging a subject using a medical imaging apparatus; acquiring a medical image of the subject according to the accelerated image acquisition scheme using the medical imaging apparatus; applying a deep network model to the medical image to improve the quality of the medical image; and outputting an improved quality image of the subject, for analysis by a physician.

A method of tumor detection and segmentation accepts a first Whole Slide Image (WSI) having a first resolution; creates a corresponding second WSI having a second resolution lower than the first resolution; applies an adaptive thresholding technique to the second WSI to create a background removal mask background; applies the mask to the first WSI to provide a third WSI with extracted patches, characterized by a third resolution, greater than the second resolution and lower than the first resolution; uses a first machine learning system on the third WSI to create a heat map at the third resolution, indicating a subset of the patches likely to include one or more clusters of tumor cells; and uses a second machine learning system on the first WSI and the heat map to segment each patch in a corresponding output image at the first resolution, outlining one or more corresponding clusters.

Items are identified in a waste stream for purposes of recycling, using deterministic and/or probabilistic techniques. Imagery of the waste stream from multiple viewpoints permit creation of a 3D depth draped image representation, from which one or more 2D planes can be synthesized. Phase-coherent patches of recoverable encoded data can be identified from among soiled and crumpled object surfaces, and used in combination to recover object identification information. Recognition of certain items can trigger further image processing that is specific to such items. (Detection of a catsup bottle, for example, can trigger image analysis to discern the presence of catsup residue.) Information about recognized objects can be provided to external data customers, e.g., to track grey market diversion of particular products into unlicensed territories. These and other features and advantages, which can be used alone or in combination, are detailed herein.