TY - JOUR
T1 - Remotely Sensing Image Fusion Based on Wavelet Transform and Human Vision System
AU - Lin, Hui
AU - Tian, Yanfeng
AU - Pu, Ruiliang
AU - Liang, Liang
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - image fusion
KW - wavelet transform
KW - human visual characteristics
KW - brightness metric
KW - texture metric
KW - root mean square rule
UR - https://digitalcommons.usf.edu/geo_facpub/1348
UR - https://pdfs.semanticscholar.org/c995/de6a1aa4f659930918d6b0bf7bd499be4005.pdf
M3 - Article
VL - 8
JO - International Journal of Signal Processing, Image Processing and Pattern Recognition
JF - International Journal of Signal Processing, Image Processing and Pattern Recognition
ER -