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
Effect of Pansharpening in Fusion Based Change Detection of Snow Cover Using Convolutional Neural Networks
TL;DR: An improvement of 0.184 and 0.267 in the kappa coefficient was observed for the overall changes in the snow cover and at the ridges, respectively, based on the results from the proposed approach.
Journal ArticleDOI
Multi-focus image fusion dataset and algorithm test in real environment
TL;DR: A multi-focus image fusion dataset and algorithm test in real environment and the results show promising results in terms of accuracy and efficiency.
Journal ArticleDOI
MGFuse: An Infrared and Visible Image Fusion Algorithm Based on Multiscale Decomposition Optimization and Gradient-Weighted Local Energy
TL;DR: Zhang et al. as mentioned in this paper proposed the MGFuse algorithm, which is a novel fusion algorithm that utilizes multiscale decomposition optimization and gradient-weighted local energy.
Survey on Image Fusion: Hand Designed to Deep Learning Algorithms
Heena Patel,Kishor Upla +1 more
TL;DR: Deep learning algorithms such as convolutional neural network (CNN), deep autoencoder (DAE), and deep belief networks (DBN) with different category of images such as multi-modal, multi-resolution,multi-temporal and multi-focus have been proposed for image fusion.
Journal ArticleDOI
Structural similarity preserving GAN for infrared and visible image fusion
Zhang Di,Zhang Di,Yong Zhou,Yong Zhou,Zhao Jiaqi,Zhao Jiaqi,Zhou Ziyuan,Zhou Ziyuan,Rui Yao,Rui Yao +9 more
TL;DR: This paper proposes an end-to-end fusion framework based on structural similarity preserving GAN (SSP-GAN) to learn a mapping of the fusion tasks for visible and infrared images, and redesigns the network architecture of multi-modal image fusion meticulously.
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.
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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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