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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Dissertation
A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition
TL;DR: Experiments show that the fused output from the camera system not only outperforms the single modality images for face recognition, but that the adaptive fusion methods used produce consistent increases in recognition accuracy under varying pose, lighting and with the presence of eyeglasses.
Journal ArticleDOI
Medical image fusion based on extended difference-of-Gaussians and edge-preserving
Journal ArticleDOI
VSP-Fuse: Multifocus Image Fusion Model Using the Knowledge Transferred From Visual Salience Priors
TL;DR: Zhang et al. as discussed by the authors proposed a randomized approach to generate massive training sets and pseudo-labels based on limited unlabeled data, and two attention modules are designed based on isometric domain transformation (IDT) in the traditional edge-preservation field.
Journal ArticleDOI
DDFN: a depth-differential fusion network for multi-focus image
Limai Jiang,Hui Fan,Jinjiang Li +2 more
Journal ArticleDOI
Multi-scale siamese networks for multi-focus image fusion
Pan Wu,Zhen Hua,Jinjiang Li +2 more
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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