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-focus image fusion based on quad-tree decomposition and edge-weighted focus measure
TL;DR: In this article , a new sum of edge-weighted modified Laplacian (SEWML) is proposed based on the sum of SML, which is used as a focus measure to detect the focus information of the source image, and this improved focus measure is more robust than SML.
Journal ArticleDOI
Multimodal Medical Supervised Image Fusion Method by CNN.
TL;DR: Zhang et al. as mentioned in this paper proposed a multimode medical image fusion with CNN and supervised learning, in order to solve the problem of practical medical diagnosis, which greatly improves the fusion effect, image detail clarity, and time efficiency.
Journal ArticleDOI
Suspect Multifocus Image Fusion Based on Sparse Denoising Autoencoder Neural Network for Police Multimodal Big Data Analysis
Jin Wang,Yanfei Gao +1 more
TL;DR: In this article, a sparse denoising autoencoder neural network is used to extract features and learn fusion rules and reconstruction rules simultaneously, and the initial decision graph of the multifocus image is taken as a prior input to learn the rich detailed information of the image.
Journal ArticleDOI
An infrared and visible image fusion algorithm based on ResNet-152
Journal ArticleDOI
Texture analysis-based multi-focus image fusion using a modified Pulse-Coupled Neural Network (PCNN)
Liubing Jiang,Dian Zhang,Li Che +2 more
TL;DR: Experimental results demonstrate that the proposed method effectively preserves the source images information while delivering good image 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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