4 results about "Artifact reduction" patented technology

The application discloses a sequence image fusion method for rotation synthetic aperture computational imaging, and is based on the imaging mechanism of a rotation synthetic aperture optical remote sensing system, and proposes an end-to-end image fusion network based on a visual Transformer. Intra-frame self-attention calculation in a space-time information extraction module can more effectively process information of objects of different scales in a remote sensing image. At the same time, an inter-frame mutual attention is used to replace an explicit alignment module, so that the correlation between pixels at similar positions in different frames can be adaptively captured, and the generation of artifacts can be reduced. A visual sliding window Transformer module is used in a space-time information fusion module of the fusion network, time domain information is fully fused through the strong modeling capability of the Transformer itself, additional information in a low-quality image sequence can be fully utilized, and characteristics prior and data input are provided for actual on-orbit application of the rotation synthetic aperture system.

This application relates to a spatial-frequency decoupled fluorescence-guided surgical image super-resolution reconstruction method, belonging to the field of image analysis technology. The method includes: constructing a super-resolution reconstruction network, which comprises a generator and a discriminator; the generator includes a shallow feature extraction module, multiple sequentially connected efficient two-stream decoupling structures, and a PixelShuffle upsampling layer; training the super-resolution reconstruction network based on a training dataset to obtain a trained generator; and inputting the low-resolution FGS image to be processed into the trained generator to obtain a super-resolution image. This application achieves synergistic optimization of FGS image noise suppression, artifact reduction, and detail preservation through two-stream decoupling and adaptive gating mechanisms, overcoming the drawback of generative adversarial networks (GANs) easily generating artifacts in medical images, resulting in reconstruction results that better meet realism requirements.

The present application provides an ultrasonic time series data processing apparatus and a non-transitory storage medium. A Doppler processing section (18) or a beam data processing section (20) generates object time series data based on a received beam data string from a receiving section (16). An artifact prediction section (38) predicts a kind of artifact generated by the object time series data by inputting the object time series data to a learned artifact prediction learner (32). An artifact reduction section (40) performs artifact reduction processing based on the predicted kind of artifact. A display control section (24) notifies a user of a corrected ultrasonic image (62) on which the artifact reduction processing is performed or a prediction result of the artifact prediction section (38).

The presently disclosed subject matter addresses the intrinsic limitation that artifact- free ground-truth X-ray images cannot be physically acquired, restricting the use of direct supervised learning for artifact correction. To overcome this constraint, the disclosure provides multiple mechanisms for developing machine-learning models dedicated to attenuating overlapping-layer artifacts in radiographs.