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
MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson’s disease diagnosis
TL;DR: Wang et al. as discussed by the authors 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
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 make full use of the multiscale features of the image.
Journal ArticleDOI
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy.
TL;DR: Zhang et al. as discussed by the authors presented an optimization-based approach to reduce defocus spread effects of the real-world multi-focus images, where each local region in the fused image should be similar to the sharpest one among source images.
Journal ArticleDOI
Deep fusion prior for plenoptic super-resolution all-in-focus imaging
TL;DR: Gu et al. as discussed by the authors proposed a dataset-free unsupervised framework named deep fusion prior (DFP) to address the multifocus image fusion (MFISRF), particularly for plenoptic SR all-in-focus imaging.
Proceedings ArticleDOI
Multifocal Image Fusion Based on Pseudo-Siamese Network and Spatial Frequency
TL;DR: In this article , a pseudo-Siamese network is used as an encoder to capture deep features in captured partially focused images, and the original decision-making image is obtained by filtering the extracted deep features through spatial frequency.
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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