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
Book ChapterDOI
An Approach for Fusion of Thermal and Visible Images
TL;DR: This work presents a novel architecture to fuse thermal and visible images using deep learning algorithms, which outperforms on qualitative and quantitative assessment and compares the method with other state-of-the-art methods.
Journal ArticleDOI
IFSepR: A General Framework for Image Fusion Based on Separate Representation Learning
TL;DR: In this paper , a multi-branch encoder with contrastive constraints is built to learn the common and private features of paired images, and a general fusion rule is designed to integrate the private features, then combining the fused private features and the common feature are fed into the decoder, reconstructing the fused image.
Journal ArticleDOI
Infrared and Visible Image Fusion with Significant Target Enhancement
Xing Huo,Yinping Deng,Kun Shao +2 more
TL;DR: Wang et al. as mentioned in this paper proposed an infrared and visible image fusion model based on significant target enhancement, aiming to inject thermal targets from infrared images into visible images to enhance target saliency while retaining important details in visible images.
Journal ArticleDOI
Deep Learning L2 Norm Fusion for Infrared & Visible Images
TL;DR: The proposed architecture collect more pixel values from both infrared and visible image and the fused image looks more natural as it contain more textual content and accomplishes a noteworthy performance with the existing models.
Journal ArticleDOI
Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
TL;DR: This paper proposes an entirely new multi-focus image fusion method based on decision map and sparse representation (DMSR), and shows that the proposed method is superior to the other five fusion methods, both in terms of visual effect and quantitative evaluation.
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