14 results about "Artifact reduction" patented technology

The invention discloses a sparse visual angle three-dimensional reconstruction method and system based on voxel grid constraint, and belongs to the technical field of visual three-dimensional reconstruction. Constructing a three-dimensional voxel grid of a self-adaptive scene scale based on point cloud distribution, and dividing the point cloud to the corresponding voxel grid; fusing the geometric features of the fast point feature histogram and the confidence score in the grid, generating a geometric confidence comprehensive measure, and screening key points to initialize Gaussian primitives; designing a voxel grid constrained gradient clipping strategy, limiting Gaussian primitive error diffusion through a distance attenuation coefficient, and adaptively optimizing grid distribution in combination with a dynamic grid deletion and addition mechanism; and finally, carrying out iterative training by using a 3D Gaussian splash radiation field loss function to realize high-fidelity static scene reconstruction under a sparse view angle. According to the method, scene geometric priori is introduced, an optimization strategy based on voxel grid constraint is designed to effectively control excessive diffusion or drift of Gaussian primitives, generation of artifacts is reduced, and meanwhile, the situation that robustness is reduced due to the influence of priori quality is avoided.

Disclosed are systems and techniques for image demosaicing with minimal artifacts. The techniques include computing a color value within a first color space for a first pixel of a plurality of pixels based at least on color values within the first color space of a first group of neighboring pixels of the first pixel and computing a first chrominance value within a second color space for the first pixel based at least on the computed color value within the first color space. The techniques include computing a luminance value within the second color space for the first pixel based at least on the first chrominance value within the second color space and converting the luminance value within the second color space and the first chrominance value within the second color space to an output pixel value within a third color space.

A pixel includes a semiconductor substrate that includes a floating diffusion region and a photodiode region. The pixel also includes, between a front surface of the semiconductor substrate and a back surface opposing the front surface: a first trench and a second trench adjacent to the first trench in a separation direction that is both (a) parallel to the front surface and (b) in a plane that is perpendicular to the front surface. Each of the first and second trench (a) is between the floating diffusion region and the photodiode region and (b) extends into the semiconductor substrate from the front surface. In the separation direction, a top average-separation between the first and second trench, at depths between the front surface and a first depth in the semiconductor substrate, exceeds a bottom average-separation between the first and second trench, at depths exceeding the first depth.

Disclosed are systems and circuits for image demosaicing with minimal artifacts. The circuits are to compute a color value within a first color space for a first pixel of a plurality of pixels based at least on color value within the first color space of a first group of neighboring pixels of the first pixel and compute a first chrominance value within a second color space for the first pixel based at least on the computed color value within the first color space. The circuits are also to compute a luminance value within the second color space for the first pixel based at least on the first chrominance value within the second color space and convert the luminance value within the second color space and the first chrominance value within the second color space to an output pixel value within a third color space.

A computer-implemented method of reducing artifacts in multi-channel magnetic resonance (MR) images is provided. The method includes receiving a plurality of sets of MR images acquired by a radio-frequency (RF) coil assembly having a plurality of channels. Each set of MR images includes a plurality of slices of MR images acquired by one of the plurality of channels. The method also includes estimating a plurality of sets of artifacts in the plurality of sets of MR images by inputting the plurality of sets of MR images into a neural network model. Each set of artifacts corresponds to the one of the plurality of channels. The method further includes reducing artifacts in the plurality of sets of MR images based on estimated artifacts, deriving MR images of reduced artifacts by combining the MR images of reduced artifacts, and outputting the MR images of reduced artifacts.

The application discloses a kind of dual-domain constraint hyperspectral image reconstruction methods based on deep learning, it is related to the technical field of artificial intelligence and computational imaging.The application simultaneously imposes constraint in spectral reconstruction domain and sparse coefficient domain, while ensuring that its inherent sparse structure conforms to physical prior, the complement and verification of dual-domain information significantly improve the fidelity of reconstruction result, reduce artifact and noise;Using deep neural network to realize reconstruction, only once forward propagation is needed for new compression measurement value to output reconstruction result, which greatly reduces the computational complexity, improves the reconstruction speed, and has the potential for real-time processing;The sparsity physical prior is explicitly integrated into the network learning goal, which can effectively resist noise interference and reduce the influence of noise on the reconstruction result;Through back propagation algorithm, the dual-head deep neural network is optimized and trained end-to-end, and the best mapping relationship is automatically learned, which greatly reduces the operation difficulty and application threshold of the method.

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.

Systems and methods for artifact reduction in ultrasound imaging comprising acquiring a sequence of ultrasound images using an ultrasound probe along an acquisition plane, tracking a movement of the ultrasound probe along the acquisition plane and a movement of one or more artifacts in the sequence of ultrasound images, identifying one or more artifacts in the sequence of ultrasound images using the movement of the ultrasound probe along the acquisition plane and the movement of the one or more artifacts, wherein the movement of the one or more artifacts is different from the movement of the ultrasound probe along the acquisition plane, and correcting the sequence of ultrasound images, by at least one of adjusting a gain of the artifacts or stitching artifact areas of the sequence of ultrasound images with non-artifact areas of the sequence of ultrasound images.

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 invention relates to an image demosaicing technique with reduced artifacts, and specifically discloses a system and technique for image demosaicing with minimal artifacts. The techniques include calculating color values of a first pixel of a plurality of pixels within a first color space based at least on color values of a first set of adjacent pixels of the first pixel within the first color space; and calculating a first chromatic value of the first pixel in the second color space at least based on the calculated color value in the first color space. The techniques include calculating a luminance value of a first pixel within a second color space based at least on a first chromatic value within the second color space; and converting the luminance value within the second color space and the first chromatic value within the second color space into an output pixel value within a third color space.

For reconstruction in sampling-based imaging, such as reconstruction in MR imaging, an iterative, multiple-mapping based hierarchal machine-learned network reconstruction may produce artifact corrected images based on under-sampled scans. Two or more mappings may be used to reduce the presence of artifacts, in some cases including localized low-noise-contribution artifacts, relative to reconstructions based on fully-sampled scans.

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).

A computer-implemented method for artifact reduction in additive manufacturing includes monitoring an image of a printing process of an object being printed to detect stringing on the object and recognizing strings on the object in the image. The strings are mapped on the object on a map within a frame of reference relative to the object. The strings are removed from the object in accordance with the map.

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.