Remotely Sensing Image Fusion Based on Wavelet Transform and Human Vision System

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image fusion, wavelet transform, human visual characteristics, brightness metric, texture metric, root mean square rule


Wavelet transform has many good characteristics, used extensively in image fusion. In recent years, many algorithms have been developed, but there exist some inherent defects such as image blur, burr phenomenon, zigzag boundaries and image discontinuity. In this theory, without considering disadvantages of HVS, especially fused image should preserve brightness and texture features which are the most sensitive to eye, so a new algorithm combining them is proposed. Firstly, by calculating brightness and texture metrics in different wavelet decomposition subimages. And then, by using root mean square rule to get fused low frequency and high frequency coefficients respectively. Finally, performing inverse wavelet transform by the concatenation of low frequency and high frequency to gain fused image. In order to evaluate different algorithms, the assessment metric based on HVS is adopted, which is a more comprehensive and effective measure. Experiments merging IKONOS Pan image(resolution is 1 meter) with multispectral image (resolution is 4 meter) show that the proposed algorithm is the best on brightness, contrast, texture, definition, resolution, object edge regardness of visual effect and objective metric, also verifing human visual characteristic to be considered in image fusion.

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Citation / Publisher Attribution

International Journal of Signal Processing, Image Processing and Pattern Recognition, v. 8, issue 7, p. 291-298