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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

Multi-Focus Image Fusion for Full-Field Optical Angiography

TL;DR: Wang et al. as mentioned in this paper proposed an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency, which significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets.
Journal ArticleDOI

DGS-Fuse: unsupervised image fusion network combining global information

TL;DR: Zhang et al. as discussed by the authors proposed an encoder-decoder network, which combines pixel loss function, multiscale structural similarity loss function and total variation loss function to further reduce the detail loss in the image reconstruction process.
Journal ArticleDOI

Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches

TL;DR: In this article , the authors proposed different CNN and CNN-LSTSM-based fusion models using physiological signals (SRAD dataset) and multimodal data (AffectiveROAD dataset).
Proceedings ArticleDOI

Unsupervised Image Fusion Using Deep Image Priors

TL;DR: In this article , a new image fusion technique was proposed that extends DIP to fusion tasks formulated as inverse problems. But the original design of DIP is hard to be generalized to multi-image processing problems, particularly for image fusion.
Journal ArticleDOI

An end-to-end medical image fusion network based on Swin-transformer

TL;DR: Wang et al. as mentioned in this paper proposed an end-to-end image fusion network built based on Swin-Transformer mechanism, which can fuse well the local and long-range (or global context) information about images.
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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