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
Multi-band images synchronous fusion based on NSST and fuzzy logical inference
TL;DR: Experimental results demonstrate that the proposed method not only effectively enhances targets and preserves details by successfully merging the far-infrared, near-inf infrared, and visible images, but also ensures improvements in the various quantitative parameters compared to existing methods.
Journal ArticleDOI
Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation
TL;DR: A post-processing step based on multi-guided filtering and morphological operations is suggested to polish the decision map of fusion and further improve the fusion results.
Journal ArticleDOI
Infrared and visible image fusion and denoising via ℓ2−ℓp norm minimization
Li Liu,Luping Xu,Houzhang Fang +2 more
TL;DR: A new and effective variational model based on half-quadratic splitting iteration is used to solve the complex optimization problem and can achieve a superior performance compared with existing fusion methods in both subjective and objective assessments.
Journal ArticleDOI
Combined multiscale segmentation convolutional neural network for rapid damage mapping from postearthquake very high-resolution images
TL;DR: Experimental results show that the proposed CMSCNN method can reflect the multiscale information of complex scenes and obtain satisfied classification results for mapping postearthquake damage using VHR remote sensing images.
Journal ArticleDOI
Dual-tree biquaternion wavelet transform and its application to color image fusion
Guo Liqiang,Cao Xiang,Liu Lian +2 more
TL;DR: The proposed dual-tree biquaternion wavelet transform for color image decomposition and reconstruction based on the proposed transformation can be implemented through a quaternion-valuedDual-tree filter bank.
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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