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
A multi-view image fusion algorithm for industrial weld
TL;DR: Wang et al. as discussed by the authors proposed a novel multi-view image fusion algorithm based on deep learning, which uses an autoencoder network structure, and its innovation lies in a parallel branch network with lightweight structure and strong generalization ability.
Journal ArticleDOI
Infrared and Low-Light Visible Image Fusion Based on Hybrid Multiscale Decomposition and Adaptive Light Adjustment
Journal ArticleDOI
Adaptive low light visual enhancement and high-significant target detection for infrared and visible image fusion
Journal ArticleDOI
Real-Time Visible-Infrared Image Fusion using Multi-Guided Filter
TL;DR: A multi-guided filter is a modification of the guided filter for multiple guidance images and a real-time image fusion method that is able to perform real- time processing and synthesizes flicker-free video.
Journal ArticleDOI
CT and MRI image fusion algorithm based on hybrid ℓ0ℓ1 layer decomposing and two-dimensional variation transform
TL;DR: Li et al. as discussed by the authors proposed a novel CT and MRI image fusion algorithm based on hybrid l 0 l 1 layer decomposition and two-dimensional variation transforms to reduce the loss of complementary features.
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.
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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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