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
Infrared and visible images fusion by using sparse representation and guided filter
TL;DR: Experimental results demonstrate the proposed infrared and visible images fusion method outperforms other popular approaches in terms of subjective perception and objective metrics.
Journal ArticleDOI
An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST
Li Junwu,Binhua Li,Yaoxi Jiang +2 more
TL;DR: The results show that the proposed algorithm fuses the images with clear edges, prominent targets, and good visual perception, and it outperforms state-of-the-art image fusion algorithms.
Journal ArticleDOI
Multi-channel fusion convolutional neural network to classify syntactic anomaly from language-related ERP components
TL;DR: The purpose of the current paper is to propose a classification technique based on data-driven approach to detect syntactic anomaly from language-related ERP components and demonstrate that the proposed method provides more than 92% classification accuracy.
Journal ArticleDOI
Medical image fusion based on improved multi-scale morphology gradient-weighted local energy and visual saliency map
Yi Zhang,Mingming Jin,Gang Huang +2 more
TL;DR: Wang et al. as mentioned in this paper proposed a multimodal medical image fusion method, where the original image is decomposed into high-frequency and low-frequency information by non-subsampled shearlet transform (NSST).
Journal ArticleDOI
Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain
TL;DR: This paper presents an adaptive fusion framework of infrared and visual images using saliency detection and an improved dual-channel pulse-coupled neural network (ID-PCNN) in the local non-subsampled shearlet transform (LNSST) domain that exhibits superior fusion performance and is more effective than typical fusion techniques.
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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