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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Journal ArticleDOI
Robust extension of light fields with probable 3D distribution based on iterative scene estimation from multi-focus images
TL;DR: In this paper , the authors proposed an iterative approach to perform pseudo-scene reconstruction based on multi-focus images for estimating the 4D light fields of the scene robustly.
Journal ArticleDOI
Blurring-Effect-Free CNN Network of Structural Edge for Focus Stacking
TL;DR: This work proposes a novel convolutional neural network (BEF-CNN) to restore blurring-effect-free image patches in order to enhance all-in-focus performance and is the first work to utilize CNN to generate all- in-focus image directly instead of pixel-to-pixel correspondence with depthmap.
Journal ArticleDOI
Multi-level receptive field feature reuse for multi-focus image fusion
Limai Jiang,Hui Fan,Jinjiang Li +2 more
TL;DR: An end-to-end deep network, which includes an encoder and a decoder, which is a pseudo-Siamese network that extracts the same and different feature sets by using the features of double encoder, then reuses the shallow features and finally forms the coding.
Journal ArticleDOI
Multifocus image fusion using a convolutional elastic network
TL;DR: The experimental results show that the proposed method overcomes the shortcomings of low spatial resolution and ambiguity in multifocus image fusion and achieves better contrast and clarity.
Proceedings ArticleDOI
FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion
TL;DR: This work presents a novel deep hierarchical variational autoencoder called FusionVAE that can serve as a basis for many fusion tasks and demonstrates that this approach outperforms traditional methods significantly.
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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