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
Infrared and Visible Image Fusion Based on Two-scale Edge Preservation Decomposition and Edge Detection
TL;DR: In this article , a fusion algorithm based on two-scale edge preservation decomposition and edge detection is proposed to obtain a clear and natural night-vision fusion image efficiently, which can effectively avoid mutual interference of different characteristic images and suppress the generation of artifacts and noise.
Journal ArticleDOI
An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion
Hongzhe Liu,Hua Yan +1 more
Proceedings ArticleDOI
Salient Object Detection based on CNN Fusion of Two Types of Saliency Models
Muhammad Umair Hassan,Dongmei Niu,Xiuyang Zhao,Shakil Ahamed Shohag,Yingjun Ma,Mingxuan Zhang +5 more
TL;DR: A new approach that is based on convolutional neural network fusion strategy to combine the saliency maps generated by high-dimensional color transform and salient object detection integrating discriminative regional features methods is introduced.
Proceedings ArticleDOI
DDRICFuse:An Infrared and Visible Image Fusion Network Based on Dual-branch Dense Residual And Infrared Compensation
TL;DR: In this article , a dual-branch dense residual residual infrared and visible image fusion network based on auto-encoder is proposed to improve the overall performance of the fusion image, an infrared feature compensation network is added that can compensate salient radiation features of the infrared image.
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.
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