This article proposes a multispectral (MS) and panchromatic (PAN) images fusion approach exploiting local spatial information by using fuzzy c-means clustering algorithm based on the Markov random field (MRFFCM). The standard principal component analysis (PCA) technique is first employed to transform the MS images into principal component spaces to extract the first principal component (PC1). Then, we decompose the PAN image using the à trous wavelet transform to get the high frequency detailed information and the approximation of the PAN image. In the process, the local relationship is employed through MRFFCM between the two to produce a fused PC1 by choosing the saliency and significant coefficients. The fused MS image is generated after the detailed information has been incorporated with the fused PC1 and finally the inverse PCA is implemented. Experimental results demonstrate that the proposed approach improves the quality of fused images both qualitatively and quantitatively.
To exact the more directional information and important detail information from the images effectively, a novel image fusion algorithm for SAR and gray visible image based on the Hidden Markov Model in the Non-subsample Shearlet Transform (NSST) domain is proposed. In NSST domain, the low frequency coefficients are fused by standard deviation. Meanwhile, the NHMT model is built to train the high frequency coefficients. After that, the energy of gradient is used to select the trained coefficients. Then, the low frequency and high frequency images are fused by inverse transformation of NSST to get the final image. Finally, the simulation proves that compared with other mufti-scale HMT models and traditional NSST fusion strategy, the proposed method in this paper can promote the fusion quality and enhance the information of the images, reducing noise as well.
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