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
Proceedings ArticleDOI
Deep Learning Framework For Mobile Microscopy
Anatasiia Kornilova,Mikhail Salnikov,Olga Novitskaya,Maria Begicheva,Egor Sevriugov,Kirill Shcherbakov,Valeriya Pronina,Dmitry V. Dylov +7 more
TL;DR: In this article, a CNN model for stable in-focus/out-of-focus classification, modified DeblurGAN architecture for image deblurring, and FuseGAN model for combining infocus parts from multiple images to boost the detail.
Journal ArticleDOI
FCGP: Infrared and Visible Image Fusion via Joint Contrast and Gradient Preservation
Huibin Yan,Shuoyao Wang +1 more
TL;DR: Wang et al. as mentioned in this paper employed a structure tensor measurement to characterize the similarity between the fused image and the infrared image in terms of thermal radiation information, to better integrate visible appearance details.
Journal ArticleDOI
A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network
TL;DR: In this article, the authors proposed a new method based on the hybrid of convolutional neural network and radial basis function neural network (RBFNN) to synthesize the projection image.
Journal ArticleDOI
Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain
Liangliang Li,Hongbing Ma +1 more
TL;DR: Wang et al. as discussed by the authors proposed a novel multimodal medical image fusion method using pulse coupled neural network (PCNN) and a weighted sum of eight-neighborhood-based modified Laplacian (WSEML) integrating guided image filtering (GIF) in non-sub-sampled contourlet transform (NSCT) domain.
Patent
A multi-focus image fusion method based on improved convolution neural network
Kong Weiwei,Lyu Lintao,Wu Wei +2 more
TL;DR: In this paper, a multi-focus image fusion method based on an improved convolution neural network is proposed, which comprises the steps of: 1, setting the weight of an improved CNN, 2, processing the convolution layer of the improved CNN and 3, performing pooling layer treatment for the CNN to obtain the final fusion result.
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