In this paper, we present a nonlocal structure tensor for feature description and apply it to the pixel level image fusion
within the wavelet framework. Local geometric shape information in wavelet coefficients can be extracted and
recognized by structure tensor. The structure tensor element is processed by use of the nonlocal means filter before
calculating its eigen-values. With the eigen-values of two source data, an adaptive weight function is employed to
reconstruct new detail coefficients of the fused image. Experimental results show the performance of the proposed
scheme.
In this paper, a structure tensor based approach is proposed for multi-focus image fusion within the wavelet
framework. Structure tensor is employed to extract local features in detail sub-bands. A nonlinear flow based
on the trace of the structure tensor matrix is applied to matrix element before calculating the eigenvalues. The
source data with larger eigenvalue contains more geometric features. An adaptive weight function is constructed
to yield new detail coefficients of the fused image. Experimental results show that the proposed scheme improves
performance compared to some related wavelet approaches.
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