Journal ArticleDOI
Pixel Convolutional Neural Network for Multi-Focus Image Fusion
TLDR
Experimental results demonstrate that the proposed p-CNN is competitive with or even outperforms the state-of-the-art methods in terms of both subjective visual perception and objective evaluation metrics.About:
This article is published in Information Sciences.The article was published on 2017-12-01. It has received 213 citations till now. The article focuses on the topics: Image fusion & Convolutional neural network.read more
Citations
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Journal ArticleDOI
IFCNN: A general image fusion framework based on convolutional neural network
TL;DR: The experimental results show that the proposed model demonstrates better generalization ability than the existing image fusion models for fusing various types of images, such as multi-focus, infrared-visual, multi-modal medical and multi-exposure images.
Journal ArticleDOI
Infrared and visible image fusion based on target-enhanced multiscale transform decomposition
TL;DR: Qualitative and quantitative experimental results on publicly available datasets demonstrate that the proposed target-enhanced multiscale transform (MST) decomposition model for infrared and visible image fusion can generate fused images with clearly highlighted targets and abundant details.
Journal ArticleDOI
Image fusion meets deep learning: A survey and perspective
TL;DR: In this paper, a comprehensive review and analysis of latest deep learning methods in different image fusion scenarios is provided, and the evaluation for some representative methods in specific fusion tasks are performed qualitatively and quantitatively.
Journal ArticleDOI
Ensemble of CNN for multi-focus image fusion
TL;DR: The obtained experimental results indicate that the proposed CNNs based network is more accurate and have the better decision map without post-processing algorithms than the other existing state of the art multi-focus fusion methods which used many post- processing algorithms.
Journal ArticleDOI
Multi-focus image fusion: A Survey of the state of the art
TL;DR: A comprehensive overview of existing multi-focus image fusion methods is presented and a new taxonomy is introduced to classify existing methods into four main categories: transformdomain methods, spatial domain methods, methods combining transform domain and spatial domain, and deep learning methods.
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
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
The Laplacian Pyramid as a Compact Image Code
Peter J. Burt,Edward H. Adelson +1 more
TL;DR: A technique for image encoding in which local operators of many scales but identical shape serve as the basis functions, which tends to enhance salient image features and is well suited for many image analysis tasks as well as for image compression.
Journal ArticleDOI
Contour Detection and Hierarchical Image Segmentation
TL;DR: This paper investigates two fundamental problems in computer vision: contour detection and image segmentation and presents state-of-the-art algorithms for both of these tasks.
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