Journal ArticleDOI
Simultaneous image fusion and denoising with adaptive sparse representation
Yu Liu,Zengfu Wang +1 more
TLDR
Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment.Abstract:
In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment.read more
Citations
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Journal ArticleDOI
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.
Journal ArticleDOI
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.
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.
Journal ArticleDOI
Deep learning for pixel-level image fusion: Recent advances and future prospects
TL;DR: This survey paper presents a systematic review of the DL-based pixel-level image fusion literature, summarized the main difficulties that exist in conventional image fusion research and discussed the advantages that DL can offer to address each of these problems.
Journal ArticleDOI
DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion
TL;DR: A new end-to-end model, termed as dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions, which establishes an adversarial game between a generator and two discriminators.
References
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