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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.
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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.

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Citations
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Journal ArticleDOI

MFDetection: A highly generalized object detection network unified with multilevel heterogeneous image fusion

TL;DR: Zhang et al. as mentioned in this paper proposed a multi-level fusion detection network (MFDetection), which fused multi-scale feature maps of visible and infrared images extracted from the feature extraction network and then applied to detection, which greatly improves the detection accuracy.
Journal ArticleDOI

A novel multisource pig-body multifeature fusion method based on Gabor features

TL;DR: A novel infrared and visible image fusion method for pig-body segmentation and temperature detection is proposed in non-subsampled contourlet transform (NSCT) domain, named as NSCT-GF, which lays a foundation for accurately measuring the temperature of pig- body.
Journal ArticleDOI

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An overview.

TL;DR: A comprehensive survey of existing medical image fusion methods is presented to establish a concrete foundation for developing more valuable fusion methods for medical diagnosis and future directions for better diagnosis.
Journal ArticleDOI

Quaternion higher-order singular value decomposition and its applications in color image processing

TL;DR: The quaternion-based higher-order singular value decomposition (QHOSVD) as mentioned in this paper is a proper tensor generalization of the QSVD, and a proper quaternionsentiment generalisation of the standard HOSVD, which can be used in various visual data processing with color pixels.
Journal ArticleDOI

AT-GAN: A generative adversarial network with attention and transition for infrared and visible image fusion

TL;DR: Zhang et al. as mentioned in this paper proposed a generative adversarial network with intensity attention modules and semantic transition modules to extract key information from multimodal images, which can adaptively learn features fusion and image reconstruction synchronously.
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

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

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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