A system and method are provided for denoising images. A standard image module is configured to generate a standard anatomy feature and a standard noise feature from a standard image and reconstruct the standard image from the standard anatomy feature and the standard noise feature. A reduced quality image module is configured to generate a reduced quality anatomy feature and a reduced quality noise feature from a reduced quality image, and reconstruct the reduced quality image from the reduced quality anatomy feature and the reduced quality noise feature. A loss calculation module is provided for calculating loss metrics at least partially based on a comparison between 1) the reconstructed standard image and the standard image, and 2) the reconstructed reduced quality image and the reduced quality image. Upon providing the standard image module with the reduced quality anatomy feature, the standard image module outputs a reconstructed standard transfer image.

Systems and methods for image correction are provided. The systems and methods may obtain raw data of a target object. The systems and methods may determine, based on the raw data of the target object, a target phase. The systems and methods may generate a first image corresponding to the target phase. The systems and methods may determine, using a preset evaluation tool, an image quality evaluation result of the first image. In response to determining that the image quality evaluation result of the first image does not satisfy a preset condition, the systems and methods may generate a set of corrected raw sub-data of the target object corresponding to the target phase by correcting a set of raw sub-data of the target object corresponding to the target phase. The systems and methods may generate, based on the set of corrected raw sub-data of the target object, a corrected image corresponding to the first image.

The present disclosure relates to systems and methods for image processing. The methods may include obtaining imaging data of a subject, generating a first image based on the imaging data, and generating at least two intermediate images based on the first image. At least one of the at least two intermediate images may be generated based on a machine learning model. And the at least two intermediate images may include a first intermediate image and a second intermediate image. The first intermediate image may include feature information of the first image, and the second intermediate image may have lower noise than the first image. The methods may further include generating, based on the first intermediate image and at least one of the first image or the second intermediate image, a target image of the subject.

An image restoration method and apparatus are provided. The image restoration method includes acquiring a target image, and acquiring a restoration image of the target image from an image restoration model to which the target image and pixel position information of the target image are input.

An apparatus acquires a low-noise image, which is training data for training an image processing model, and a plurality of high-noise images corresponding to the same scene in the low-noise image data and each having different noise patterns. The apparatus calculates each of errors between a plurality of estimated output values, which is acquired by inputting a different one of the plurality of high-noise images to the image processing model and the low-noise image. Then, the apparatus calculates an anti-noise stability based on an error between the plurality of estimated output values, and trains the image processing model using a loss function including the errors between the plurality of estimated output values and the low-noise image and the anti-noise stability.

Provided in the present application are an image noise reduction method and device, an imaging system, and a non-transitory computer-readable storage medium. The image noise reduction method includes: processing, based on a first deep learning network, an original scanned object image to acquire a noise image corresponding to the original scanned object image; and acquiring a denoised image based on the original scanned object image and the noise image; wherein the first deep learning network is obtained by training based on low signal-to-noise ratio images and high signal-to-noise ratio images.

Disclosed is a novel CT denoising method using deep learning to boost the image signal-to-noise ratio for improved disease detection in patients with acute ischemic stroke (AIS). This method reduced the original CT image noise significantly better than BM3D, with SNR improvements in GM, WM, and DG by 2.47?, 2.83?, and 2.64? respectively and CNR improvements in DG/WM and GM/WM by 2.30? and 2.16? respectively. Scans denoised by the proposed model are shown to be visually clearer with preserved anatomy. The disclosed deep learning model significantly reduces image noise and improves signal-to-noise and contrast-to-noise ratios in 380 unseen head NCCT cases.

A mechanism for generating a partially denoised image. A residual noise image, obtained by processing an image using a convolutional neural network, is weighted. The blending or combination of the weighted residual noise image and the (original) image generates the partially denoised image.

A method for diagnostic imaging with reduced contrast agent dose uses a deep learning network (DLN) [114] that has been trained using zero-contrast [100] and low-contrast [102] images as input to the DLN and full-contrast images [104] as reference ground truth images. Prior to training, the images are pre-processed [106, 110, 118] to co-register and normalize them. The trained DLN [114] is then used to predict a synthesized full-dose contrast agent image [116] from acquired zero-dose and low-dose images.

An image processing apparatus is provided that includes: an obtaining unit configured to obtain a first medical image of a subject; and an image quality improving unit configured to generate a second medical image with image quality higher than image quality of different regions including a first region and a second region that is different from the first region in the obtained first image, using the obtained first image as input data that is input into a learned model.