Abstract: A system for generating depth information from low-resolution images is configured to access a plurality of image frames capturing an environment, identify a first group of image frames from the plurality of image frames, and generate a first image comprising a first composite image of the environment using the first group of image frames as input. The first composite image has an image resolution that is higher than an image resolution of the image frames of the first group of image frames. The system is also configured to obtain a second image of the environment, where parallax exists between a capture perspective associated with the first image and a capture perspective associated with the second image. The system is also configured to generate depth information for the environment based on the first image and the second image.

Abstract: A system for generating depth information from low-resolution images is configured to access a plurality of image frames capturing an environment, identify a first group of image frames from the plurality of image frames, and generate a first image comprising a first composite image of the environment using the first group of image frames as input. The first composite image has an image resolution that is higher than an image resolution of the image frames of the first group of image frames. The system is also configured to obtain a second image of the environment, where parallax exists between a capture perspective associated with the first image and a capture perspective associated with the second image. The system is also configured to generate depth information for the environment based on the first image and the second image.

Abstract: A system for generating depth information from low-resolution images is configured to access a plurality of image frames capturing an environment, identify a first group of image frames from the plurality of image frames, and generate a first image comprising a first composite image of the environment using the first group of image frames as input. The first composite image has an image resolution that is higher than an image resolution of the image frames of the first group of image frames. The system is also configured to obtain a second image of the environment, where parallax exists between a capture perspective associated with the first image and a capture perspective associated with the second image. The system is also configured to generate depth information for the environment based on the first image and the second image.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: An apparatus and method are provided to correct motion artifacts in magnetic resonance imaging (MRI) data by finding and removing motion-corrupted encodes. The MRI data non-uniformly sample k-space using a series of shots, each including one or more encodes. The motion-corrupted encodes/shots are identified by omitting respective encodes/shots from the MRI data when reconstructing respective images using a compressed-sensing (CS) method. The image quality is improved for those reconstructed images in which the motion-corrupted encodes are omitted, whereas all other images include the motion-corrupted encodes and exhibit the motion artifact. Assuming a minority of encodes/shots are corrupted by motion, the images improved by omitting the motion-corrupted encodes can be identified as outliers. Once, the motion-corrupted encodes are identified and excluded from the final MRI dataset, a final, high-resolution image is reconstructed using the final MRI dataset.

Abstract: An apparatus and method are provided to simultaneously provide good image quality and fast image reconstruction from magnetic resonance imaging (MRI) data by selecting an appropriate value for the regularization parameter used in compressed sensing (CS) image reconstruction. In CS reconstruction a high-resolution image can be reconstructed from randomized undersampled data by imposing sparsity in multi-scale transformation (e.g., wavelet) domain. Further, in the transformation domain, a threshold can be determined between signal and noise levels of the transform coefficients. A regularization parameter based on this threshold scales the regularization term, which imposes sparsity, relative to the data fidelity term in an objective function, thereby balancing the tradeoff between noise and smoothing.

Abstract: An apparatus and method are provided to correct motion artifacts in magnetic resonance imaging (MRI) data by finding and removing motion-corrupted encodes. The MRI data non-uniformly sample k-space using a series of shots, each including one or more encodes. The motion-corrupted encodes/shots are identified by omitting respective encodes/shots from the MRI data when reconstructing respective images using a compressed-sensing (CS) method. The image quality is improved for those reconstructed images in which the motion-corrupted encodes are omitted, whereas all other images include the motion-corrupted encodes and exhibit the motion artifact. Assuming a minority of encodes/shots are corrupted by motion, the images improved by omitting the motion-corrupted encodes can be identified as outliers. Once, the motion-corrupted encodes are identified and excluded from the final MRI dataset, a final, high-resolution image is reconstructed using the final MRI dataset.

Abstract: Devices, systems, and methods for generating a medical image receive scan data, wherein the scan data is defined in an acquisition space; perform a first reconstruction process to generate a lower-resolution image based on the scan data, wherein the first reconstruction process uses a subset of the scan data; and perform a second reconstruction process to generate a higher-resolution image based on the scan data, wherein the second reconstruction process uses more of the scan data than the subset of the scan data.

Abstract: Devices, systems, and methods for generating a medical image receive scan data, wherein the scan data is defined in an acquisition space; perform a first reconstruction process to generate a lower-resolution image based on the scan data, wherein the first reconstruction process uses a subset of the scan data; and perform a second reconstruction process to generate a higher-resolution image based on the scan data, wherein the second reconstruction process uses more of the scan data than the subset of the scan data.