Journal ArticleDOI
Pixel-level image fusion
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TLDR
It is concluded that although various image fusion methods have been proposed, there still exist several future directions in different image fusion applications and the researches in the image fusion field are still expected to significantly grow in the coming years.About:
This article is published in Information Fusion.The article was published on 2017-01-01. It has received 871 citations till now. The article focuses on the topics: Image fusion & Night vision.read more
Citations
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FusionGAN: A generative adversarial network for infrared and visible image fusion
TL;DR: This paper proposes a novel method to fuse two types of information using a generative adversarial network, termed as FusionGAN, which establishes an adversarial game between a generator and a discriminator, where the generator aims to generate a fused image with major infrared intensities together with additional visible gradients.
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Infrared and visible image fusion methods and applications: A survey
TL;DR: This survey comprehensively survey the existing methods and applications for the fusion of infrared and visible images, which can serve as a reference for researchers inrared and visible image fusion and related fields.
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Multi-focus image fusion with a deep convolutional neural network
TL;DR: 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.
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DenseFuse: A Fusion Approach to Infrared and Visible Images
Hui Li,Xiaojun Wu +1 more
TL;DR: A novel deep learning architecture for infrared and visible images fusion problems is presented, where the encoding network is combined with convolutional layers, a fusion layer, and dense block in which the output of each layer is connected to every other layer.
Journal ArticleDOI
Image Fusion With Convolutional Sparse Representation
TL;DR: A recently emerged signal decomposition model known as convolutional sparse representation (CSR) is introduced into image fusion to address this problem, motivated by the observation that the CSR model can effectively overcome the above two drawbacks.
References
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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.
Journal ArticleDOI
Scale-space and edge detection using anisotropic diffusion
Pietro Perona,Jitendra Malik +1 more
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
A model of saliency-based visual attention for rapid scene analysis
TL;DR: A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented, which breaks down the complex problem of scene understanding by rapidly selecting conspicuous locations to be analyzed in detail.
Journal ArticleDOI
The Laplacian Pyramid as a Compact Image Code
Peter J. Burt,Edward H. Adelson +1 more
TL;DR: A technique for image encoding in which local operators of many scales but identical shape serve as the basis functions, which tends to enhance salient image features and is well suited for many image analysis tasks as well as for image compression.
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
TL;DR: It is shown that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex.
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