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Complex Wavelet Structural Similarity: A New Image Similarity Index

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5 Author(s)
Sampat, M.P. ; Dept. of Neurology, Univ. of California San Francisco, San Francisco, CA, USA ; Zhou Wang ; Gupta, S. ; Bovik, A.C.
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We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.

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Image Processing, IEEE Transactions on  (Volume:18 ,  Issue: 11 )