most citedReference-based Magnetic Resonance Image Reconstruction Using Texture Transformer

1 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation

Shao-Yuan Lo, Wei Wang, Jim Thomas +3

Monocular depth estimation (MDE) has attracted intense study due to its low cost and critical functions for robotic tasks such as localization, mapping and obstacle detection. Supe…

cs.CV20221 cited

Deep Semantic Statistics Matching (D2SM) Denoising Network

Kangfu Mei, Vishal M. Patel, Rui Huang

The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learnin…

eess.IV20221 cited

Learning to restore images degraded by atmospheric turbulence using uncertainty

Rajeev Yasarla, Vishal M. Patel

Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index o…

cs.CV2022

Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination

Yiqun Mei, Pengfei Guo, Vishal M. Patel

In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Large domain discrepancy makes HFR a difficult pr…

cs.CV20211 cited

Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer

Pengfei Guo, Vishal M. Patel

Deep Learning (DL) based methods for magnetic resonance (MR) image reconstruction have been shown to produce superior performance in recent years. However, these methods either onl…