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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Proceedings ArticleDOI
Integration of Bi-dimensional Empirical Mode Decomposition With Two Streams Deep Learning Network for Infrared and Visible Image Fusion
TL;DR: A novel fusion strategy is proposed here to analyze the spatial inter-dependency between infrared and visible features and precisely preserve the correlative information from the source images to keep the standard details with reduced artifacts in the fused image.
Posted Content
A Robust Non-Linear and Feature-Selection Image Fusion Theory.
TL;DR: A multi-source image fusion framework that combines illuminance factors and attention mechanisms that effectively combines traditional image features and modern deep learning features is proposed.
Journal ArticleDOI
TPP: Deep learning based threshold post-processing multi-focus image fusion method
TL;DR: Wang et al. as mentioned in this paper proposed a new deep learning method called threshold post processing (TPP), which contains three architecturally different channels, and three different weighted maps can be obtained for the same input.
Journal ArticleDOI
When Multi-Focus Image Fusion Networks Meet Traditional Edge-Preservation Technology
Journal ArticleDOI
Multichannel cross-fusional convolutional neural networks
TL;DR: Wang et al. as discussed by the authors proposed multichannel concat-fusional convolutional neural networks (McCfCNNs) with fusion types of R+G +B/R+G+B+B/r+B
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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