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
Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis
TL;DR: Experimental results demonstrate that the proposed CS-MCA model can outperform some benchmarking and state-of-the-art SR-based fusion methods in terms of both visual perception and objective assessment.
Journal ArticleDOI
A survey of infrared and visual image fusion methods
TL;DR: It is concluded that although various IR and VI image fusion methods have been proposed, there still exist further improvements or potential research directions in different applications of IR andVI image fusion.
Journal ArticleDOI
FusionDN: A Unified Densely Connected Network for Image Fusion.
TL;DR: A new unsupervised and unified densely connected network for different types of image fusion tasks, termed as FusionDN, which obtains a single model applicable to multiple fusion tasks by applying elastic weight consolidation to avoid forgetting what has been learned from previous tasks when training multiple tasks sequentially.
Journal ArticleDOI
Medical image fusion method by deep learning
TL;DR: The experimental results prove the superiority of the proposed method in terms of visual quality and a variety of quantitative evaluation criteria, and greatly improve the fusion effect, image detail clarity and time efficiency.
Proceedings ArticleDOI
Infrared and Visible Image Fusion using a Deep Learning Framework
Hui Li,Xiaojun Wu,Josef Kittler +2 more
TL;DR: Li et al. as discussed by the authors proposed an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images, which achieved state-of-the-art performance in both objective assessment and visual quality.
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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