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Robust image watermarking based on multiband wavelets and empirical mode decomposition
- IEEE Trans. Image Processing
, 1956
"... Abstract—In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavel ..."
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Cited by 8 (2 self)
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Abstract—In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavelet coefficients, in the proposed scheme, we embed the watermark bits in the mean trend of some middle-frequency subimages in the wavelet domain. We further select appropriate dilation factor and filters in the multiband wavelet transform to achieve better performance in terms of perceptually invisibility and the robustness of the watermark. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and ConvFilter attacks. The comparison analysis demonstrate that our scheme has better performance than the watermarking schemes reported recently. Index Terms—Empirical mode decomposition (EMD), image watermarking, multiband wavelets transformation (MWT). I.
High-resolution image reconstruction with displacement errors: A framelet approach
- Int. J. Imaging Syst. Technol
, 2004
"... High-resolution image reconstruction arises in many applications, such as remote sensing, surveillance, and medical imaging. The model of Bose and Boo [2] can be viewed as the passage of the high-resolution image through a blurring kernel built from the tensor product of a univariate low-pass filter ..."
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Cited by 7 (2 self)
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High-resolution image reconstruction arises in many applications, such as remote sensing, surveillance, and medical imaging. The model of Bose and Boo [2] can be viewed as the passage of the high-resolution image through a blurring kernel built from the tensor product of a univariate low-pass filter of the form � 1 1 2 + ɛ, 1, ·· · , 1, 2 − ɛ � , where ɛ is the displacement error. When the number L of low-resolution sensors is even, tight frame symmetric framelet filters were constructed in [8] from this low-pass filter using the unitary extension principle of [43]. The framelet filters do not depend on ɛ, and hence the resulting algorithm reduces to that of the case where ɛ =0. Furthermore, the framelet method works for symmetric boundary conditions. This greatly simplifies the algorithm. However, both the design of the tight framelets and extension to symmetric boundary are only for even L and cannot be applied to the case when L is odd. In this paper, we design tight framelets and derive a tight framelet algorithm with symmetric boundary conditions that work for both odd and even L. An analysis of the convergence of the algorithms is also given. The details of the implementations of the algorithm are also given. 1
Tight
, 2004
"... frame: an efficient way for high-resolution image reconstruction ..."
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Tight Frame Based Method for High-Resolution Image Reconstruction
, 2010
"... We give a comprehensive discussion on high-resolution image reconstruction based on tight frame. We first present the tight frame filters arising from the problem of high-resolution image reconstruction and the associated matrix representation of the filters for various boundary extensions. We then ..."
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We give a comprehensive discussion on high-resolution image reconstruction based on tight frame. We first present the tight frame filters arising from the problem of high-resolution image reconstruction and the associated matrix representation of the filters for various boundary extensions. We then propose three algorithms for high-resolution image reconstruction using the designed tight frame filters and show analytically the properties of these algorithms. Finally, we numerically illustrate the efficiency of the proposed algorithms for natural images. 1 High-Resolution Image Reconstruction Model The problem of high-resolution image reconstruction is to reconstruct a high-resolution (HR) image from multiple, under-sampled, shifted, degraded and noisy frames where each frame differs from the others by some sub-pixel shifts. The problem arises in a variety of scientific, medical, and engineering applications. The problem of HR image reconstruction is a hot field. In the past few years, two special issues on the topic
