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Haitao Yin

Bio: Haitao Yin is an academic researcher from Hunan University. The author has contributed to research in topics: Sparse approximation & Image fusion. The author has an hindex of 9, co-authored 11 publications receiving 1439 citations. Previous affiliations of Haitao Yin include Nanjing University of Posts and Telecommunications & Nanjing University.

Papers
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Journal ArticleDOI
TL;DR: 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.

871 citations

Journal ArticleDOI
TL;DR: A novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR), where the geometrical structure of atoms is modeled as the graph regularization and the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively.
Abstract: Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

310 citations

Journal ArticleDOI
TL;DR: By comparing with six well-known methods in terms of several universal quality evaluation indexes with or without references, the simulated and real experimental results on QuickBird and IKONOS images demonstrate the superiority of the proposed remote sensing image fusion method.
Abstract: Remote sensing image fusion can integrate the spatial detail of panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MS) image to produce a fused MS image with high spatial resolution. In this paper, a remote sensing image fusion method is proposed with sparse representations over learned dictionaries. The dictionaries for PAN image and low-resolution MS image are learned from the source images adaptively. Furthermore, a novel strategy is designed to construct the dictionary for unknown high-resolution MS images without training set, which can make our proposed method more practical. The sparse coefficients of the PAN image and low-resolution MS image are sought by the orthogonal matching pursuit algorithm. Then, the fused high-resolution MS image is calculated by combining the obtained sparse coefficients and the dictionary for the high-resolution MS image. By comparing with six well-known methods in terms of several universal quality evaluation indexes with or without references, the simulated and real experimental results on QuickBird and IKONOS images demonstrate the superiority of our method.

275 citations

Journal ArticleDOI
Haitao Yin1, Shutao Li1
TL;DR: A novel mul- timodal image fusion scheme based on the joint sparsity model which is derived from the distributed compressed sensing, which demonstrates the effectiveness of the proposed method in terms of visual effect and quantitative fusion evaluation indexes.
Abstract: Image fusion combines multiple images of the same scene into a single image which is suitable for human perception and practical applications. Different images of the same scene can be viewed as an ensemble of intercorrelated images. This paper proposes a novel mul- timodal image fusion scheme based on the joint sparsity model which is derived from the distributed compressed sensing. First, the source images are jointly sparsely represented as common and innovation components using an over-complete dictionary. Second, the common and innovations sparse coefficients are combined as the jointly sparse coeffi- cients of the fused image. Finally, the fused result is reconstructed from the obtained sparse coefficients. Furthermore, the proposed method is compared with some popular image fusion methods, such as multiscale transform-based methods and simultaneous orthogonal matching pursuit- based method. The experimental results demonstrate the effectiveness of the proposed method in terms of visual effect and quantitative fusion evaluation indexes. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

111 citations

Journal ArticleDOI
TL;DR: A novel framework for simultaneous image fusion and super-resolution based on the use of sparse representations is proposed, and consists of three steps: low-resolution source images are interpolated and decomposed into high- and low-frequency components, and sparse coefficients from these components are computed and fused by using image fusion rules.

93 citations


Cited by
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Journal ArticleDOI
TL;DR: A general image fusion framework by combining MST and SR to simultaneously overcome the inherent defects of both the MST- and SR-based fusion methods is presented and experimental results demonstrate that the proposed fusion framework can obtain state-of-the-art performance.

952 citations

Journal ArticleDOI
TL;DR: 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.

871 citations

Journal ArticleDOI
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.

853 citations

Journal ArticleDOI
Jiayi Ma1, Yong Ma1, Chang Li1
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.

849 citations

Journal ArticleDOI
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.

826 citations