X
Xun Chen
Researcher at University of Science and Technology of China
Publications - 230
Citations - 7083
Xun Chen is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 27, co-authored 143 publications receiving 3549 citations. Previous affiliations of Xun Chen include University of British Columbia & Hefei University of Technology.
Papers
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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.
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Image Fusion With Convolutional Sparse Representation
TL;DR: A recently emerged signal decomposition model known as convolutional sparse representation (CSR) is introduced into image fusion to address this problem, motivated by the observation that the CSR model can effectively overcome the above two drawbacks.
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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.
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Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain
TL;DR: Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.
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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.