Z
Zengfu Wang
Researcher at University of Science and Technology of China
Publications - 142
Citations - 3809
Zengfu Wang is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer facial animation & Pixel. The author has an hindex of 18, co-authored 129 publications receiving 2596 citations. Previous affiliations of Zengfu Wang include Hefei Institutes of Physical Science & Chinese Academy of Sciences.
Papers
More filters
Journal ArticleDOI
Multi-focus image fusion with a deep convolutional neural network
TL;DR: A new multi-focus image fusion method is primarily proposed, aiming to learn a direct mapping between source images and focus map, using a deep convolutional neural network trained by high-quality image patches and their blurred versions to encode the mapping.
Journal ArticleDOI
Deep learning for pixel-level image fusion: Recent advances and future prospects
TL;DR: This survey paper presents a systematic review of the DL-based pixel-level image fusion literature, summarized the main difficulties that exist in conventional image fusion research and discussed the advantages that DL can offer to address each of these problems.
Journal ArticleDOI
Simultaneous image fusion and denoising with adaptive sparse representation
Yu Liu,Zengfu Wang +1 more
TL;DR: Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment.
Journal ArticleDOI
Infrared and visible image fusion with convolutional neural networks
TL;DR: This paper proposes an infrared fusion image that combines infrared and visible images of the same scene to generate a composite image which can provide a more comprehensive description of the scene.
Proceedings ArticleDOI
Joint multi-label multi-instance learning for image classification
TL;DR: This work proposes an integrated multi- label multi-instance learning (MLMIL) approach based on hidden conditional random fields (HCRFs), which simultaneously captures both the connections between semantic labels and regions, and the correlations among the labels in a single formulation.