147 citations · 311 across the 12 of their papers we have counts for
12 papers
Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography
Zihan Chen, Tianrui Liu, Jun-Jie Huang +3
The existing image steganography methods either sequentially conceal secret images or conceal a concatenation of multiple images. In such ways, the interference of information amon…
A Fast Automatic Method for Deconvoluting Macro X-ray Fluorescence Data Collected from Easel Paintings
Su Yan, Jun-Jie Huang, Herman Verinaz-Jadan +3
Macro X-ray Fluorescence (MA-XRF) scanning is increasingly widely used by researchers in heritage science to analyse easel paintings as one of a suite of non-invasive imaging techn…
DURRNet: Deep Unfolded Single Image Reflection Removal Network
Jun-Jie Huang, Tianrui Liu, Zhixiong Yang +3
Single image reflection removal problem aims to divide a reflection-contaminated image into a transmission image and a reflection image. It is a canonical blind source separation p…
Mixed X-Ray Image Separation for Artworks with Concealed Designs
Wei Pu, Jun-Jie Huang, Barak Sober +5
In this paper, we focus on X-ray images of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artis…
WINNet: Wavelet-inspired Invertible Network for Image Denoising
Jun-Jie Huang, Pier Luigi Dragotti
Image denoising aims to restore a clean image from an observed noisy image. The model-based image denoising approaches can achieve good generalization ability over different noise…
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Tianrui Liu, Qingjie Meng, Jun-Jie Huang +3
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content…
LINN: Lifting Inspired Invertible Neural Network for Image Denoising
Jun-Jie Huang, Pier Luigi Dragotti
In this paper, we propose an invertible neural network for image denoising (DnINN) inspired by the transform-based denoising framework. The proposed DnINN consists of an invertible…
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
Jingyuan Xia, Shengxi Li, Jun-Jie Huang +2
In this paper, we propose a novel solution for non-convex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that sp…
Learning Deep Analysis Dictionaries for Image Super-Resolution
Jun-Jie Huang, Pier Luigi Dragotti
Inspired by the recent success of deep neural networks and the recent efforts to develop multi-layer dictionary models, we propose a Deep Analysis dictionary Model (DeepAM) which i…
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Tianrui Liu, Wenhan Luo, Lin Ma +3
Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, detecting small-scaled pedestrians and occlu…
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
Tianrui Liu, Jun-Jie Huang, Tianhong Dai +2
Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, robustly detecting pedestrians with a large…
Reconstruction of FRI Signals using Deep Neural Network Approaches
Vincent C. H. Leung, Jun-Jie Huang, Pier Luigi Dragotti
Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited continuous signals that have a small number of free parameters, such as a…