Search Results for author:
Found 17 papers, 4 papers with code
Improving Neural Radiance Field using Near-Surface Sampling with Point Cloud Generation
This paper proposes a near-surface sampling framework to improve the rendering quality of NeRF.
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data
Vehicle control data is constructed by many hours of human driving, and it is challenging to construct large vehicle control datasets.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
Accelerated MRI With Deep Linear Convolutional Transform Learning
Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple applications.
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames
Second, conventional monocular SLAM uses inappropriate mapping factors such as dynamic objects and low-parallax areas in mapping.
Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles
Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane.
An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT
We propose a new INN architecture for DECT material decomposition.
Momentum-Net for Low-Dose CT Image Reconstruction
We also investigated the spectral normalization technique that applies to image refining NN learning to satisfy the nonexpansive NN property; however, experimental results show that this does not improve the image reconstruction performance of Momentum-Net.
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Numerical results with phantom data show that applying faster numerical solvers to model-based image reconstruction (MBIR) modules of BCD-Net leads to faster and more accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy, compared to the state-of-the-art MBIR method using learned transforms; BCD-Net achieves better image quality, compared to a state-of-the-art iterative NN architecture, ADMM-Net.
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision.
Improved low-count quantitative PET reconstruction with an iterative neural network
Image reconstruction in low-count PET is particularly challenging because gammas from natural radioactivity in Lu-based crystals cause high random fractions that lower the measurement signal-to-noise-ratio (SNR).
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets.
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
In "extreme" computational imaging that collects extremely undersampled or noisy measurements, obtaining an accurate image within a reasonable computing time is challenging.
Convolutional Analysis Operator Learning: Acceleration and Convergence
This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems.
Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform
Our results with the extended cardiac-torso (XCAT) phantom data and clinical chest data show that, for sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST-$\ell_1$ improves the quality of reconstructed images compared to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$ prior with learned ST.
Convolutional Dictionary Learning: Acceleration and Convergence
However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems.
