Abstract
In this paper the ill-posedness of restoring lost samples is discussed in the two dimensional case. The restoration algorithm by Shannon’s Sampling Theorem is analyzed. A regularized restoring algorithm for two dimensional band-limited signals is presented. The convergence of the regularized restoring algorithm is studied and compared with the restoration algorithm by Shannon’s sampling theorem in the two dimensional case.
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The author would like to express appreciation to professor Malcolm R. Adams for his help in the revision of this paper.
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Appendix
Appendix
Proof of Theorem 1.2
In (1) we take the convolution with
we obtain
Let \(t_1=0, ~t_2=0\), solve for \(f(0, 0)\), then we obtain the formula (2).
Proof of Lemma 3.3
By the Cauchy inequality and Lemma 3.2
Proof of Lemma 3.4
By Lemma 3.1, the first two terms are of the order \(O(\delta )+O(\delta \ln \frac{1}{\alpha })\). By using the estimation (6) the third term is of the order \(O(\delta \ln ^2 \frac{1}{\alpha })\).
Proof of Lemma 3.5
By the Cauchy inequality
Then by Lemma 3.2, \(I^2=O(\alpha )\).
Proof of Lemma 3.6
By the Cauchy inequality
Then by Lemma 3.2, \(J_1^2=O(\alpha ^\frac{1}{2})\).
Proof of Lemma 3.7
\(J_2=I_1+I_2+I_3\) where
Here
by Lemma 3.3. For the same reason \(I_2=O(\alpha ^{\frac{1}{4}})\).
by Lemma 3.5 and by Lemma 3.6.
Proof of Theorem 3.1
\(f_\alpha (0, 0)-f_E(0, 0)\)
where \(J_2=O(\alpha ^{\frac{1}{4}})\) is the same as in Lemma 3.7 and
is the same as in Lemma 3.4.
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Chen, W. Regularized restoration for two dimensional band-limited signals. Multidim Syst Sign Process 26, 665–675 (2015). https://doi.org/10.1007/s11045-013-0263-2
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DOI: https://doi.org/10.1007/s11045-013-0263-2