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
Multi-focus image fusion with half weighted gradient and self-similarity
TL;DR: A new multi-focus image fusion method called half weighted gradient and self-similarity (HWGSS) is proposed, based on the advantages of multi-scale weighted gradient, while abandoning the shortcomings of weighted gradient.
Proceedings ArticleDOI
A Convolutional Neural Network Based on Double-tower Structure for Underwater Terrain Classification
TL;DR: A double-tower convolutional neural network has been designed to implement end-to-end underwater terrain classification to improve the efficiency and accuracy of terrain classification.
Journal ArticleDOI
Medical Image Fusion and Denoising Algorithm Based on a Decomposition Model of Hybrid Variation-Sparse Representation
TL;DR: Wang et al. as mentioned in this paper proposed a new image layer decomposition model based on hybrid variation-sparse representation and weighted Schatten p-norm, which can effectively remove noise while retaining the gradient information without color distortion.
Proceedings ArticleDOI
Infrared and visible image fusion using multi-resolution convolution neural network
Yun Ge,Guodong Jing +1 more
TL;DR: A multi-resolution convolution neural network, which is constructed by multi-scale convolution operators, which will calculate features in different scales and can prove that the multi-fusion method is an effective method.
Journal ArticleDOI
A Two-To-One Deep Learning General Framework for Image Fusion
TL;DR: This paper proposes a general image fusion framework based on an improved convolutional neural network that not only has generality and stability but also has some strengths in subjective visualization and objective evaluation.
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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