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
More filters
Journal ArticleDOI
MRI and SPECT Image Fusion Using a Weighted Parameter Adaptive Dual Channel PCNN
TL;DR: Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods in terms of both visual quality and objective assessment.
Journal ArticleDOI
Fully Convolutional Network-Based Multifocus Image Fusion
TL;DR: Experimental results show that the proposed novel multifocus image fusion method based on a fully convolutional network learned from synthesized multifocus images can achieve superior fusion performance in both human visual quality and objective assessment.
Journal ArticleDOI
Coupled GAN With Relativistic Discriminators for Infrared and Visible Images Fusion
TL;DR: The proposed RCGAN can produce a faithful fused image, which can efficiently persevere the rich texture from visible images and thermal radiation information from infrared images, and also shows a clear advantages over other deep learning-based fusion methods.
Journal ArticleDOI
An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
TL;DR: The existing deep convolutional spatiotemporal fusion network (DCSTFN) is refined and improved to further boost model prediction accuracy and enhance image quality, and the fusion result is improved considerably with brand-new network architecture and a novel compound loss function.
Journal ArticleDOI
Multi-focus image fusion using boosted random walks-based algorithm with two-scale focus maps
TL;DR: This paper proposes to estimate a focus map directly from the two-scale imperfect observations (focus maps) obtained using a small and large-scale focus measures, and finds that this method is equivalent to solving an alternate objective function, enabling a great boost both in computational efficiency and estimation result.
References
More filters
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.
Related Papers (5)
A general framework for image fusion based on multi-scale transform and sparse representation
Yu Liu,Shuping Liu,Zengfu Wang +2 more