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Name: Qiegen Liu
Type: User
Company: Nanchang University
Bio: My current research interest is sparse representation, deep learning and their applications in image processing, computer vision and MRI reconstruction.
Name: Qiegen Liu
Type: User
Company: Nanchang University
Bio: My current research interest is sparse representation, deep learning and their applications in image processing, computer vision and MRI reconstruction.
Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
Comparison of deep learning methods in imaging: Supervised, unsupervised and self-supervised learning
Dense Connected Cascade Network (DCCN) for MRI Reconstruction
Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
Variable Augmented Neural Network for Decolorization and Multi-Exposure Fusion
Deep Learning Papers on Medical Image Analysis
Diffusion Models for Medical Imaging
Dual-domain Mean-reverting Diffusion Model-enhanced Temporal Compressive Coherent Diffraction Imaging
Densely connected network for impulse noise removal
Detail-Preserving MR Reconstruction via Multiple Diffusion Models
DTM: Diffusion Transformer Model Guided by Compact Prior in Low-dose PET Reconstruction
Distribution-transformed Network for Impulse Noise Removal
Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
基于深度能量模型的低剂量CT重建
MRI Reconstruction Using Energy-Based Model
Enhanced Denoising Auto-Encoding Priors for Reconstruction
High-dimensional Embedding Network Derived Prior for CS-MRI
Deep Energy-based Model with f-Divergence for Parallel MRI Reconstruction
Field-of-Experts Filters Guided Tensor Completion
High-resolution iterative reconstruction at extremely low sampling rate for Fourier single-pixel imaging via diffusion model
Gradient correlation similarity for efficient contrast preserving decolorization
Solving Inverse Computational Imaging Problems Using Deep Generative Gradient of Priors
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
Convolutional Sparse Coding in Gradient Domain for MRI Reconstruction
Adaptive dictionary learning in sparse gradient domain for image recovery
Adaptive K-space Learning and High-dimensional Subsets Embedding for MRI Reconstruction
Deep Frequency-recurrent Priors for Inverse Imaging Reconstruction
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