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
High-quality image multi-focus fusion to address ringing and blurring artifacts without loss of information
TL;DR: In this article, the authors proposed a novel information preservation-based guided filtering (IPGF) method to overcome the challenges of ringing and blurring artifacts in multi-focus image fusion while preserving the input image regions.
Journal ArticleDOI
Multi-focus image fusion based on smooth and iteratively restore filter
TL;DR: An improved smooth and iteratively restore (SIR) filter is proposed to deal with the problem of in-focus image fusion and achieves better performance than existing methods in terms of both subjective and objective evaluations.
Posted Content
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i.e., a sparseto-coarse stage and a coarse-tofine stage.
Journal ArticleDOI
Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion
Yonghong Jiang,Yaning Ma +1 more
TL;DR: An optimal version of the homomorphic fusion, namely optimum homomorphic wavelet fusion (OHWF) on the hybrid particle swarm and ant colony optimization methods, is presented and illustrates that the presented method performs a desiring ability in image fusion in the case of functional and structural data.
Proceedings Article
Comparative Analysis of Image Fusion Methods
TL;DR: This paper discusses mainly the advantages and disadvantages of spatial and frequency domain methods used for the enhancement of an image which has been discussed in this paper.
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
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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