Abstract: Various methods and systems are provided for training a denoising system for a digital imaging system. The denoising system can be a deep learning denoising system formed as a blind or non-blind denoising system in which the training dataset provided to the denoising system includes a noisy image formed with simulated noise added to a clean digital image, and a reference image formed of the clean image having residual noise added thereto, where the residual noise is a fraction of the simulated noise used to form the noisy image. The use of the residual noise within the reference image of the training dataset teaches the DL network in the training process to remove less than all the noise during subsequent inferencing of digital images from the digital imaging system. By leaving selected amounts of noise in the digital images, the denoiser can be tuned to improve image attributes and texture.

Abstract: Various methods and systems are provided for denoising images. In one example, a method includes obtaining an input image and a noise map representing noise in the input image, generating, from the noise map and based on a calibration factor, a strength map, entering the input image and the strength map as input to a denoising model trained to output a denoised image based on the input image and the strength map, and displaying and/or saving the denoised image output by the denoising model.

Abstract: Systems/techniques that facilitate improved neural network inferencing efficiency with fewer parameters are provided. In various embodiments, a system can access a medical image on which an artificial intelligence task is to be performed. In various aspects, the system can facilitate the artificial intelligence task by executing a neural network pipeline on the medical image, thereby yielding an artificial intelligence task output that corresponds to the medical image. In various instances, the neural network pipeline can include respective skip connections from the medical image, prior to any convolutions, to each convolutional layer in the neural network pipeline.

Abstract: Various methods and systems are provided for x-ray imaging. In one embodiment, a method includes acquiring, with an x-ray detector, an x-ray image of a subject, determining a transformation that minimizes anti-scatter grid artifacts in the x-ray image, correcting the x-ray image according to the transformation to generate a corrected image, and outputting the corrected image. In this way, artifacts arising from a misalignment of an anti-scatter grid between the calibration and the acquisition may be reduced.