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
More filters
Journal ArticleDOI
Infrared and visual image fusion using LNSST and an adaptive dual-channel PCNN with triple-linking strength
TL;DR: A novel fusion framework for infrared and visual image that uses an adaptive dual-channel pulse-coupled neural network with triple-linking strength with adaptive linking strength in a local non-subsampled shearlet transform domain is presented.
Journal ArticleDOI
Multi-focus image fusion techniques: a survey
Shiveta Bhat,Deepika Koundal +1 more
TL;DR: A new classification scheme is developed for categorizing the existing MFIF methods and both the parametric evaluation metrics i.e. "with reference" and "without reference" have been discussed.
Journal ArticleDOI
An α-Matte Boundary Defocus Model-Based Cascaded Network for Multi-Focus Image Fusion
TL;DR: Experiments demonstrate that the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.
Journal ArticleDOI
A multiscale residual pyramid attention network for medical image fusion
TL;DR: Wang et al. as discussed by the authors proposed a multiscale residual pyramid attention network (MSRPAN) for medical image fusion, which consists of one feature extractor, fuser and reconstructor.
Journal ArticleDOI
Multimodal medical image fusion via laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy
TL;DR: In the fusion results of Alzheimer and Glioma, the disease details are much clearer than those of compared algorithms, which can provide a reliable basis for doctors to analyze disease and make pathological diagnoses.
References
More filters
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.
Related Papers (5)
A general framework for image fusion based on multi-scale transform and sparse representation
Yu Liu,Shuping Liu,Zengfu Wang +2 more