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
A new focus evaluation operator based on max–min filter and its application in high quality multi-focus image fusion
TL;DR: The concept and computational processing of MMAM is proposed, which provides a new research direction and innovative idea for multi-focus image fusion base on filter, and MMAM can embedded into state-of-the-art fusion algorithm to achieve high quality multi- focus image fusion.
Journal ArticleDOI
Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection
TL;DR: A multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images, which can achieve better visual perception according to subjective evaluation and objective indexes.
Journal ArticleDOI
A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset
TL;DR: In this article, the authors proposed a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset, and the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.
Journal ArticleDOI
Semantics-guided reconstruction of indoor navigation elements from 3D colorized points
TL;DR: A semantics-guided method for indoor navigation element reconstruction from RGB-D sensor data is proposed to effectively provide the semantic guidance for the cellular representation of the indoor space and its topological reconstruction.
Journal ArticleDOI
Deep learning-based compressed image artifacts reduction based on multi-scale image fusion
Chia-Hung Yeh,Chia-Hung Yeh,Chu Han Lin,Min Hui Lin,Li-Wei Kang,Chih Hsiang Huang,Mei-Juan Chen +6 more
TL;DR: A deep network to eliminate image compression artifacts based on image fusion in multi-scale manner based on the learning of the residuals between the received images and their corresponding clean images (ground truths).
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