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
Acoustic impedance inversion using convolutional neural network with transfer learning
TL;DR: In this article , a novel acoustic impedance inversion method is proposed to fuse the background model and the migrated reflectivity for the absolute acoustic impedance estimation in reservoir description in oil and gas industry.
Journal ArticleDOI
Fusion2Fusion: An Infrared–Visible Image Fusion Algorithm for Surface Water Environments
TL;DR: In this paper , an attention mechanism is introduced into the feature extraction to better extract features and a new way of describing the fusion task is proposed. And the relationship between the two inputs is balanced by introducing a fused image obtained by summing the infrared and visible images, which is also optimized for sky layering and water surface ripples.
Proceedings ArticleDOI
MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson’s disease diagnosis
TL;DR: Wang et al. as mentioned in this paper proposed a multiplane embedding and spatial-temporal transformer (MEST) framework for Parkinsons disease (PD) diagnosis, which can effectively integrate the rich representations from multi-modality data.
Journal ArticleDOI
Designing CNNs for Multimodal Image Restoration and Fusion via Unfolding the Method of Multipliers
TL;DR: Two multimodal models are designed which employ the proposed encoder followed by an appropriately designed decoder that maps the learned representations to the desired output, providing representations that can lead to accurate image reconstruction.
Posted Content
Benchmarking and Comparing Multi-exposure Image Fusion Algorithms
TL;DR: MeFB as discussed by the authors is a benchmark for multi-exposure image fusion which consists of a test set of 100 image pairs, a code library of 16 algorithms, 20 evaluation metrics, 1600 fused images and a software toolkit.
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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