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
Multifocus Image Fusion Based on Fast Guided Filter and Focus Pixels Detection
TL;DR: A novel simple and effective multifocus image fusion technique based on fast guided filter and focus pixels detection and a new multi-scale sum modified Laplacian technique to detect the focused pixels correctly from source images.
Journal ArticleDOI
Fast local Laplacian filtering based enhanced medical image fusion using parameter-adaptive PCNN and local features-based fuzzy weighted matrices
TL;DR: In this article, a fast local Laplacian filter (FLLF) is applied to the source images to enhance the edge information and suppress the noise artifacts, and the RGB images are converted to YUV color space to separate the Y-component.
Journal ArticleDOI
Image fusion using a multi-level image decomposition and fusion method.
Yu Tian,Wenjing Yang,Ji Wang +2 more
TL;DR: In image fusion, a novel fusion strategy based on a pretrained ResNet50 network is presented to fuse multi-level feature information from both visible and infrared images into corresponding multi- level fused feature information, so as to improve the quality of the final fused image.
Journal ArticleDOI
Parameter adaptive unit-linking dual-channel PCNN based infrared and visible image fusion
TL;DR: In this paper , a new parameter adaptive unit-linking dual-channel PCNN model was used to implement a novel fusion algorithm in the non-subsampled contourlet transform (NSCT) domain for the integration of infrared and visible images.
Journal ArticleDOI
Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform
TL;DR: Experimental results demonstrate that the proposed algorithm can integrate more background details as well as highlight the interesting region with the salient objects, which is superior to the conventional methods in objective quality evaluations and visual inspection.
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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