A system for adapting an image noise removal model based on a device processing capability receives, from a computing device, a request to adapt an image noise removal module for the computing device. The system compares a processing capability of the computing device with a threshold processing capability. The system determines whether the processing capability is greater or smaller than the threshold processing capability. In response to determining that the processing capability is greater than the threshold processing capability, the system sends a version of the image noise removal module that is adapted for computing devices with processing capabilities less than the threshold processing capability, where the version of the image noise removal module is adapted to have a number of neural network layers less than a threshold number of neural network layers.

Techniques are provided for improving image data quality, such as in functional imaging follow-up studies, using reconstruction, post-processing, and/or deep-learning enhancement approaches in a way that automatically improves analysis fidelity, such as lesion tracking fidelity. The disclosed approaches may be useful in improving the performance of automatic analysis methods as well as in facilitating reviews performed by clinician.

Adaptive imaging methods and systems for generating enhanced low light video of an object for medical visualization are disclosed and include acquiring, with an image acquisition assembly, a sequence of reference frames and/or a sequence of low light video frames depicting the object, assessing relative movement between the image acquisition assembly and the object based on at least a portion of the acquired sequence of reference video frames or the acquired sequence of low light video frames, adjusting a level of image processing of the low light video frames based at least in part on the relative movement between the image acquisition assembly and the object, and generating a characteristic low light video output from a quantity of the low light video frames, wherein the quantity of the low light video frames is based on the adjusted level of image processing of the low light video frames.

A system for generating a privacy protected image. The system may have a detection logic and obscuring logic configured to detect one or more identity-correlated object categories. The system may provide automatic selection of a type of obscuring to be used based on the detected category; wherein said automatic selection includes automatically selecting among a plurality of different, category specific types of reversible obscuring. The detection logic and obscuring logic may include an automatic category-specific selecting between irreversible obscuring and reversible obscuring. The system may comprise a privacy protected image output logic configured to generate the privacy protected image.

For medical imaging such as MRI, machine training is used to train a network for segmentation using both the imaging data and protocol data (e.g., meta-data). The network is trained to segment based, in part, on the configuration and/or scanner, not just the imaging data, allowing the trained network to adapt to the way each image is acquired. In one embodiment, the network architecture includes one or more blocks that receive both types of data as input and output both types of data, preserving relevant features for adaptation through at least part of the trained network.

An image processing method is provided. The method includes: obtaining a to-be-processed image; extracting attribute information of the to-be-processed image; determining an image processing strategy corresponding to the to-be-processed image according to the attribute information; and obtaining a target image by processing the to-be-processed image according to the image processing strategy.

An image processor for transforming an input image, the image processor being configured to implement a trained artificial intelligence model, wherein the image processor is configured to: receive the input image; based on one or both of (i) the content of the input image and (ii) features extracted from the input image, process the image by the trained artificial intelligence model to: (i) determine a set of image filters; and (ii) for each of a plurality of subregions of the image, select an image filter from the set of image filters; and for each of the plurality of subregions of the image, apply the respective image filter to the subregion or to features extracted from that subregion. This may allow for differentiable selection of filters from a discrete learnable and decorrelated group of filters to allow for content based spatial adaptations

For medical imaging such as MRI, machine training is used to train a network for segmentation using both the imaging data and protocol data (e.g., meta-data). The network is trained to segment based, in part, on the configuration and/or scanner, not just the imaging data, allowing the trained network to adapt to the way each image is acquired. In one embodiment, the network architecture includes one or more blocks that receive both types of data as input and output both types of data, preserving relevant features for adaptation through at least part of the trained network.

Systems and methods are disclosed and an example system includes a digital image receiver for receiving a digital image, and an automatic obscuration processor coupled to the image receiver and configured to determine whether the digital image includes a region that classifies as an image of a category of object and, upon a positive determination, to obscure the region and output a corresponding obscured-region digital image.

Digital signal representations of sub-images are obtained by applying an optimization process wherein a sum is minimized, the sum having a first term representing a measure of the consistency of the sum of a digital representations of sub-images with said radiographic image and wherein the second term is a sum of cost functions each describing the type of one of said sub-images.