Journal ArticleDOI
Multi-focus image fusion with a deep convolutional neural network
TLDR
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.About:
This article is published in Information Fusion.The article was published on 2017-07-01. It has received 826 citations till now. The article focuses on the topics: Image fusion & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Global-Feature Encoding U-Net (GEU-Net) for Multi-Focus Image Fusion
TL;DR: Experimental results show that the proposed GEU-Net can achieve superior fusion performance than some state-of-the-art methods in both human visual quality, objective assessment and network complexity.
Proceedings Article
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
TL;DR: A novel end-to-end residual learning framework is proposed, which formulates the depth completion as a two-stage learning task, i.e., a sparse- to-coarse stage and a coarse-tofine stage, and achieves SoTA performance in RMSE on KITTI benchmark.
Journal ArticleDOI
Edge-preserving image denoising using a deep convolutional neural network
TL;DR: Experimental results on various test images including benchmark grayscale images and medical ultrasound images demonstrate that the proposed method achieves better performance compared to some state-of-the-art denoising approaches.
Journal ArticleDOI
A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion
TL;DR: This paper proposes a generic image fusion method with a bilevel optimization paradigm, targeting on multi-modality image fusion tasks, and successfully applied it to three types of image fused tasks, including infrared and visible, computed tomography and magnetic resonance imaging, and magnetic Resonance Imaging and single-photon emission computed tomographic image fusion.
Journal ArticleDOI
A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images
Bo Li,Hong Peng,Jun Wang +2 more
TL;DR: The qualitative and quantitative experimental results demonstrate the advantage of the proposed fusion method in terms of the visual quality and fusion performance.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
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