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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Proceedings ArticleDOI
Infrared and Visible Image Fusion Based on Two-scale Edge Preservation Decomposition and Edge Detection
TL;DR: In this paper , a fusion algorithm based on two-scale edge preservation decomposition and edge detection is proposed to obtain a clear and natural night-vision fusion image efficiently, which can effectively avoid mutual interference of different characteristic images and suppress the generation of artifacts and noise.
Journal ArticleDOI
RFN-Nest: An end-to-end residual fusion network for infrared and visible images
Hui Li,Xiaojun Wu,Josef Kittler +2 more
TL;DR: Wang et al. as mentioned in this paper proposed a residual fusion network (RFN) which is based on a residual architecture to replace the traditional fusion approach, and a novel detail-preserving loss function and a feature enhancing loss function are proposed to train RFN.
Towards faithful neural fusion to infrared and visible images with a full-scale connected network
TL;DR: Wang et al. as mentioned in this paper proposed a full-scale connected-based fusion network (FSCF-Net) for infrared and visible image fusion to achieve the goal of merging image information from multiple source images into one image.
Journal ArticleDOI
LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion
TL;DR: In this paper , a local binary pattern (LBP)-based proportional input generative adversarial network (LPGAN) was proposed to fuse RGB and visible images, which can not only achieve good structural similarity but also retain richly detailed information.
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.
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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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