非负矩阵分解及其在模式识别中的应用

To overcome the problem of multi-sensor image fusion, a technique for image fusion based on non-subsampled contourlet transform (NSCT) domain improved nonnegative matrix factorization (NMF) is presented. Firstly, by using NSCT, multi-scale and multi-direction sparse decompositions of source images are performed. Then, an improved NMF technique is utilized to complete the fusion of low-frequency sub-images. The low-frequency fused image can be produced fast by the process which does not involve the randomization of the vectors W and H at all, in addition, the fusion course of high-frequency sub-images can be dealt with by use of the model of adaptive unit-fast-linking pulse coupled neural network (AUFLPCNN). Finally, the ultimate fused image can be obtained by synthesizing all sub-images with inverse NSCT. The simulated experiments show that the technique is effective.Keywords image fusion, non-subsampled contourlet transform, nonnegative matrix factorization, singular value decomposition CitationKong W W, Lei Y J, Lei Y, et al. Technique for image fusion based on non-subsampled contourlet transform domain improved NMF.

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Technique for image fusion based on non-subsampled contourlet transform domain improved NMF

Kong

Lei

Yang

et al. 2010

Sci. China Inf. Sci.

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Single image super-resolution method via refined local learning

Tang

Xiao

Liu

2015

J. Shanghai Jiaotong Univ. (Sci.)

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Recent years have witnessed the prosperity of referencebased image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes.

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A method of facial expression recognition based on Gabor and NMF

Zhou

Zhang

Mei

et al. 2016

Pattern Recognit. Image Anal.

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非负矩阵分解及其在模式识别中的应用

Cited by 3 publications

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Technique for image fusion based on non-subsampled contourlet transform domain improved NMF

Technique for image fusion based on non-subsampled contourlet transform domain improved NMF

Single image super-resolution method via refined local learning

A method of facial expression recognition based on Gabor and NMF

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