Jun-Jie Huang
Associate Professor
College of Computer Science and Technology
National University of Defense Technology (NUDT)
Changsha, Hunan, China
About
I earned my Ph.D. (2019) at the Department of Electrical and Electronic Engineering, Imperial College London, under the supervision of Prof. Pier Luigi Dragotti. I received the B.Eng. (Hons, First Class, 2013) in Electronic Engineering from The Hong Kong Polytechnic University and the M.Phil. (2015) under the supervision of Prof. Wan-Chi Siu. During 2019–2021, I was a postdoctoral research associate at Imperial College London, advised by Prof. Pier Luigi Dragotti.
Research Interests: Multi-modal processing, model-based deep learning, and computer vision.
Publications: 70+ papers in prestigious venues including IEEE TPAMI, TIP, TNNLS, TSP, TCSVT, AAAI, NeurIPS, ACM MM.
What's New
Selected Publications
A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal
unFlowS: An Unsupervised Construction Scheme of Flow Spectrum for Network Traffic Detection
Multi-view Clustering via Multi-stage Fusion
DeMPAA: Deployable Multi-Mini-Patch Adversarial Attack for Remote Sensing Image Classification
Multi-view subspace clustering via adaptive graph learning and late fusion alignment
Learning-based Reconstruction of FRI Signals
A Fast Automatic Method for Deconvoluting Macro X-ray Fluorescence Data Collected from Easel Paintings
Revealing and Reconstructing Hidden or Lost Features in Art Investigation
WINNet: Wavelet-inspired Invertible Network for Image Denoising
Mixed X-Ray Image Separation for Artworks with Concealed Designs
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
When de Prony Met Leonardo: An Automatic Algorithm for Chemical Element Extraction from Macro X-ray Fluorescence Data
Learning Deep Analysis Dictionaries for Image Super-Resolution
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Deep Phase Retrieval: Analyzing Over-Parameterization in Phase Retrieval
Photo Realistic Image Completion via Dense Correspondence
Learning Hierarchical Decision Trees for Single Image Super-Resolution
Fast Image Interpolation via Random Forests
Current Students
Former Students
Academic Talks
Selected Awards
Teaching
Convex Optimization
Spring 2023, 2024
Machine Learning
Fall 2023