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Wavelet Image Fused Based on Neighborhood Average Grads

In the field of image fusion, wavelet transform is an important method.Firstly, image is decomposed using wavelet transform. Image' s wavelet coefficients are gained. Based on average grads reflecting exiguous detail differences of image, the paper proposes a wavelet fused algorithm based on neighborhood average grads. Source images are fused, and then the result compares with others two methods such as maximum absolute value and neighborhood variance.The performance of the method is evaluated using the peak sig- nal- to- noise, signal- to- noise and the error of mean square root,and points out the prime decomposed layer. Experiment testifies that algorithm is of definite predominance.
【Fund】: 国家自然科学基金资助项目(No.60272054)
【CateGory Index】: TP391.41
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