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
Smart Mobile Microscopy: Towards Fully-Automated Digitization
TL;DR: In this article, the authors present a "smart" mobile microscope concept aimed at automatic digitization of the most valuable visual information about the specimen, which performs this through combining automated microscope setup control and classic techniques such as auto-focusing, in-focus filtering, and focus stacking.
Journal ArticleDOI
SS-SSAN: a self-supervised subspace attentional network for multi-modal medical image fusion
Proceedings ArticleDOI
Multi-model imaging detection using a learning feature fusion module
TL;DR: A feature fusion module for multi-model imaging based on deep learning that can extract more detailed information from several source images and achieve higher detection accuracy in various natural backgrounds is designed.
Proceedings ArticleDOI
Image Fusion Processing Method Based on Infrared and Visible Light
Xiaogong Lin,Ronghao Yang +1 more
TL;DR: In this paper, a basic methods of image fusion based on infrared and visible light and image processing are designed and the subjective and objective methods ofimage fusion quality evaluation are systematically introduced.
Proceedings ArticleDOI
Infrared and visible image fusion based on dense connectivity and Transformer self-encoding
TL;DR: Li et al. as discussed by the authors proposed a self-encoder based on the combination of dense connection module and transformer module, and the Transformer module can extract the non-local interaction relationship of the source image from the entire image, focusing more on the global features.
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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