Abstract
Reconstruction of signals by their Fourier (transform) samples is investigated in many mathematical/engineering problems such as the inverse Radon transform and optical diffraction tomography. This paper concerns on the reconstruction of spline-spectra signals in \(V(\hbox {sinc}_{a})\) by finitely many Fourier samples, where \(\hbox {sinc}_{a}\) is the generalized sinc function. There are two main results on this topic. When the spectra knots are known, the exact reconstruction formula conducted by finitely many Fourier samples is established in the first main theorem. When the spectra knots are unknown, in the second main theorem we establish the approximations to the spline-spectra signals also by finitely many Fourier samples. Numerical simulations are conducted to check the efficiency of the approximation.
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Acknowledgements
The authors would like to thank the reviewers for their valuable suggestions which improve the presentation of the paper. Youfa Li is supported by Natural Science Foundation of China (Nos: 61961003, 61561006, 11501132), Natural Science Foundation of Guangxi (No: 2019GXNSFAA185035) and the talent project of Education Department of Guangxi Government for one thousand Young-Middle-Aged backbone teachers.
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Communicated by Deguang Han.
Appendix
Appendix
Proof of Lemma 2.2
By the definition of \(\{\xi _{k}\}_{k=1}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}\), we have \(-\pi =\xi _{1}<\xi _{2}<\cdots < \xi _{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}=\xi _{1}+\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mu _{0}\le \pi\) and \(\lfloor \frac{2\pi }{\mu _{0}}\rfloor \ge s\). Furthermore, if \(\lfloor \frac{2\pi }{\mu _{0}}\rfloor =\frac{2\pi }{\mu _{0}}\), then
And if \(\lfloor \frac{2\pi }{\mu _{0}}\rfloor <\frac{2\pi }{\mu _{0}}\), then
We next prove that for any \(i\in \{1,2, \ldots , s\}\), there exists at least a point \(\xi _{k_{i}}\in [t_{i},t_{i+1})\) where \(k_{i}\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor +1\}\). This statement will be proved for the two cases (4.1) and (4.2), respectively.
Case (4.1): For the interval \([t_{s},t_{s+1})\), it follows from (2.3) and (4.1) that \(\xi _{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}= t_{s+1}=\pi\) which together with
leads to \(\xi _{\lfloor \frac{2\pi }{\mu _{0}}\rfloor }\ge t_{s}\). Then
For \(i=1,\) it is clear that
Allowing for (4.4) and (4.5), we just need to prove the statement for the intervals \([t_{i},t_{i+1}),i=2,\ldots ,s-1\) where \(s>2.\) Or else, if there exists \(i_{0}\in \{2, \ldots , s-1\}\) such that \(\xi _{k}\notin [t_{i_{0}},t_{i_{0}+1})\) for any \(k\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor \}\), then by (2.3) and (4.1) we have
It follows from (4.6) that there exists \(k\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor -1\}\) such that \(\xi _{k+1}-\xi _{k}>t_{i_{0}+1}-t_{i_{0}}\ge \mu _{0}\). This contradicts with \(\xi _{k+1}-\xi _{k}=\mu _{0}\). Therefore, the statement holds for the case (4.1).
Case (4.2): For the interval \([t_{s},t_{s+1})\), it follows from \(\xi _{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}=\xi _{1}+ \lfloor \frac{2\pi }{\mu _{0}}\rfloor \mu _{0}\) and \(\xi _{1}=-\pi\) that
which together with \(t_{s+1}=\pi\) and \(t_{s+1}-t_{s}\ge \mu _{0}\) leads to \(\xi _{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}>t_{s}\). Then
For \(i=1\), it is easy to prove that (4.5) still holds. By (4.5) and (4.8), we just need to prove the statement for the intervals \([t_{i},t_{i+1}),i=2,\ldots ,s-1\) where \(s>2.\) Or else, if there exists \(i_{0}\in \{2,\ldots ,s-1\}\) such that \(\xi _{k}\notin [t_{i_{0}},t_{i_{0}+1})\) for any \(k\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor +1\}\), then it follows from (2.3) and (4.2) that
Now by (4.9), there exists \(k\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor \}\) such that \(\xi _{k+1}-\xi _{k}>t_{i_{0}+1}-t_{i_{0}}\ge \mu _{0}\). This contradicts with \(\xi _{k+1}-\xi _{k}=\mu _{0}\). Summarizing what has addressed above, for any \(i\in \{1,2, \ldots , s\}\) there exists at least a point \(\xi _{k_{i}}\in [t_{i},t_{i+1})\), where \(k_{i}\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor +1\}\).
Next we consider how to determine \(\{d_{i}\}_{i=1}^{s}\). For any \(i\in \{1,\ldots , s\}\), by the above statement there exists \(\xi _{k_{i}}\in [t_{i},t_{i+1})\) where \(k_{i}\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor +1\}\). Then it follows from (2.5) that \(\xi _{k_{i}}+ 2m_{k_{i}}\pi \in {\mathcal {C}}^{m_{k_{i}}}_{i}\) with \(m_{k_{i}}\in {\mathbb {Z}}\), which together with (2.9) leads to
Based on the observed samples \(\{{\widehat{f}}(\xi _{k}+2m_{k}\pi )\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}_{k=1}\), define \(\{ {\widetilde{d}}_{k}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}_{k=1}\) by
where \(m_{k}\in {\mathbb {Z}}\). Obviously, by (4.10) and (4.11) we have
It follows from \(\xi _{1}\in [t_{1},t_{2})\) that \(d_{1}={\widetilde{d}}_{1}\). Note that \(d_{i}\ne d_{i+1}\). Then \(\{d_{i}\}^{s}_{i=2}\) can be obtained iteratively by
where \(\vartheta (i)=\min \{\vartheta (i-1)< k\le (\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1) \ \hbox {s.t.} \ {\widetilde{d}}_{k}\ne d_{i-1} \}\) with \(\vartheta (1)=1\).
Proof of Proposition 2.3
We first prove that \(\{\eta _{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}\) increases as i does. Direct observation on (2.12) (A1-A5) gives us that we just need to prove \(\xi _{k}+2\varepsilon (\mathbf{n} -1)<\xi _{k+1}\) for \(k=1,2,\ldots ,\lfloor \frac{2\pi }{\mu _{0}}\rfloor\). It follows from \(\mathbf{n} =\lceil \frac{\mu _{0}}{2\varepsilon }\rceil\) and \(\xi _{k+1}=\xi _{k}+\mu _{0}\) that \(\xi _{k+1}-\big (\xi _{k}+2\varepsilon (\mathbf{n} -1)\big )=\mu _{0}-2\varepsilon (\lceil \frac{\mu _{0}}{2\varepsilon }\rceil -1) =(\frac{\mu _{0}}{2\varepsilon }-\lceil \frac{\mu _{0}}{2\varepsilon }\rceil )2\varepsilon +2\varepsilon\). Note that \(-1<\frac{\mu _{0}}{2\varepsilon }-\lceil \frac{\mu _{0}}{2\varepsilon }\rceil \le 0\). Then
That is, \(\xi _{k}+2\varepsilon (\mathbf{n} -1)<\xi _{k+1}\). And \(\{\eta _{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}\) increases as i does.
We next prove that \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon\) for \(k=2,\ldots ,s\), where \({\widetilde{t}}_{k}\) is defined by (2.14).
Note that \(\{\eta _{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}\supseteq \{\xi _{k}\}_{k=1}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}\). Then it is easy to follow from Lemma 2.2, (2.13) and (4.12) that \(\{d_{j}\}^{s}_{j=1}\subseteq \{ {\widehat{d}}_{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}\).
Specifically, by the similar procedure for Lemma 2.2 (4.13), \(\{d_{j}\}_{j=1}^{s}\) can be determined iteratively by
where
Direct observation on (2.12) (A1–A5) and (4.14) gives us that for any \(k\in \{1, \ldots , \lfloor \frac{2\pi }{\mu _{0}}\rfloor \}\), we have
And for \(i=(k-1)\mathbf{n} +1,\ldots ,k\mathbf{n} -1\), we have
Now for \(\varepsilon \in (0,\frac{\mu _{0}}{2}]\), (4.16) and (4.17) lead to
It follows from (4.15) that \(d_{1}={\widehat{d}}_{1}=\cdots ={\widehat{d}}_{\tau (2)-1}\), which together with \(\eta _{1}=\xi _{1}\in [t_{1},t_{2})\) and (4.18) leads to \(\eta _{\tau (2)-1}\in [t_{1},t_{2})\).
On the other hand, note that \(\eta _{\tau (2)}-\eta _{\tau (2)-1}\le \mu _{0}\) and \({\widehat{d}}_{\tau (2)}=d_{2}\ne d_{1}\). Then using (4.15) again we have \(\eta _{\tau (2)}\in [t_{2},t_{3})\). For \(k=2,\ldots ,s\), we can also prove that \(\eta _{\tau (k)-1}\in [t_{k-1},t_{k})\) and \(\eta _{\tau (k)}\in [t_{k},t_{k+1})\). Consequently,
For \({\widetilde{t}}_{k}\) defined by (2.14), we next prove that \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon\). We first prove \({\widetilde{t}}_{k}\in (-\pi ,\pi )\). By the definition of \(\{\xi _{k}\}_{k=1}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}\), we have \(\xi _{1}=-\pi\) and \(\xi _{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}=\xi _{1}+\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mu _{0}\le \pi\). Recall that \(\{\eta _{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}\) increases as i does. Then by (2.12) we have
It follows from (2.14) and (4.20) that \({\widetilde{t}}_{k}\in (-\pi ,\pi )\). Now by (4.19), it is easy to prove that \(|t_{k}-{\widetilde{t}}_{k}|\le \frac{\eta _{\tau (k)}-\eta _{\tau (k)-1}}{2}\). Consequently, the proof can be concluded by (4.18).
Proof of Theorem 2.5
Some lemmas for proving Theorem 2.5
Lemma 4.1
Let \(\{\eta _{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}\) be defined as Proposition 2.3 (2.12), where \(\mu _{0}=\min \{t_{j+1}-t_{j}: j=1,\ldots ,s\}\) with \(\{t_{j}\}_{j=1}^{s+1}\) satisfying (2.3) and \(\mathbf{n} =\lceil \frac{\mu _{0}}{2\varepsilon }\rceil\) with \(\varepsilon \in (0,\frac{\mu _{0}}{2}]\). If \(\{{\widetilde{t}}_{k}\}_{k=2}^{s}\) are constructed via (2.14) such that \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon\), then
Proof
For any \(k\in \{2,\ldots ,s-1\}\), it follows from (2.14) that
Now by Proposition 2.3, \(\{\eta _{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}\) increases as i does which together with (4.15) leads to that
\(\eta _{\tau (k+1)-1}\ge \eta _{\tau (k)}\). Invoking (4.22), we have
We next prove that \({\widetilde{t}}_{k+1}-{\widetilde{t}}_{k}\ge 2\varepsilon\) for the three cases: \(\varepsilon =\frac{\mu _{0}}{2}\), \(\varepsilon \in (\frac{\mu _{0}}{4},\frac{\mu _{0}}{2})\) and \(\varepsilon \in (0,\frac{\mu _{0}}{4}]\).
If \(\varepsilon =\frac{\mu _{0}}{2}\) then \(\mathbf{n} =\lceil \frac{\mu _{0}}{2\varepsilon }\rceil =1\), which together with (2.12) leads to \(\{\eta _{i}\}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor \mathbf{n} +1}_{i=1}=\{\xi _{i}\}_{i=1}^{\lfloor \frac{2\pi }{\mu _{0}}\rfloor +1}\).
Note that \(\xi _{i+1}-\xi _{i}=\mu _{0}\) for \(i=1,\ldots ,\lfloor \frac{2\pi }{\mu _{0}}\rfloor\). Then by (4.15) we have
It follows from (4.23) and (4.24) that \({\widetilde{t}}_{k+1}-{\widetilde{t}}_{k}\ge 2\varepsilon .\)
If \(\varepsilon \in (\frac{\mu _{0}}{4},\frac{\mu _{0}}{2})\) then \(\mathbf{n} =\lceil \frac{\mu _{0}}{2\varepsilon }\rceil =2\). Allowing for \(\mathbf{n} =2\) and (2.12), we have
And by (4.19), we have
That is, \(\eta _{\tau (k+1)}-\eta _{\tau (k)-1}>\mu _{0}\).
Consequently, it follows from (4.25), (4.26) and \(\xi _{i+1}-\xi _{i}=\mu _{0}\) for \(i=1,\ldots ,\lfloor \frac{2\pi }{\mu _{0}}\rfloor\) that
Then (4.23) and (4.27) lead to \({\widetilde{t}}_{k+1}-{\widetilde{t}}_{k}>2\varepsilon .\)
If \(\varepsilon \in (0,\frac{\mu _{0}}{4}]\) then it follows from \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon\), \(|t_{k+1}-{\widetilde{t}}_{k+1}|\le \varepsilon\) and \(t_{k+1}-t_{k}\ge \mu _{0}\) that
The proof is concluded. \(\square\)
Lemma 4.2
Suppose that \(f\in V(\hbox {sinc}_{a})\) has the spline spectra such that (1.15) holds with \(d_{k}\ne d_{k+1}\) and the spectra knots \(t_{i}, i=1,\ldots ,s+1\) satisfying (2.3). Denote \({\widetilde{t}}_{1}:=-\pi\) and \({\widetilde{t}}_{s+1}:=\pi\). Let \(\varepsilon\) (being sufficiently small) and \(\{{\widetilde{t}}_{k}\}^{s}_{k=2}\) (being the approximation to \(\{t_{k}\}^{s}_{k=2}\)) be as in Proposition 2.3. Define \({\widetilde{f}}\) via (2.6) with \(\{t_{i}\}^{s+1}_{i=1}\) being replaced by \(\{{\widetilde{t}}_{i}\}^{s+1}_{i=1}\). Then for any fixed \(k\in \{2,\ldots ,s\}\), we have
Proof
We first prove that
and
for \(k=2,\ldots ,s.\) Recall in Proposition 2.3 that \(\varepsilon \in (0,\frac{\mu _{0}}{2}]\) with
and \(\{{\widetilde{t}}_{k}\}_{k=2}^{s}\) are constructed via (2.14) such that \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon \ \hbox {for}\ k=2,\ldots ,s.\) Clearly,
It follows from (4.30) that
Now by (4.31) and (4.32), it is easy to check that \(t_{k-1}\le {\widetilde{t}}_{k}-\varepsilon\) and \({\widetilde{t}}_{k}+\varepsilon \le t_{k+1}\). Then (4.28) holds. We next prove that (4.29) holds. Recall that \(\{{\widetilde{t}}_{k}\}_{k=2}^{s}\) are constructed via (2.14) such that \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon\). Then by Lemma 4.1 (4.21), we have
where \(s>2\). For \(k=2\), by \({\widetilde{t}}_{1}=-\pi =t_{1}\), \(|t_{2}-{\widetilde{t}}_{2}|\le \varepsilon\) and \(t_{2}-t_{1}\ge 2\varepsilon\) we have
Then \({\widetilde{t}}_{1}\le {\widetilde{t}}_{2}-\varepsilon\). Now (4.33) and (4.34) with \(i=2\) lead to \([{\widetilde{t}}_{2}-\varepsilon ,{\widetilde{t}}_{2}+\varepsilon )\subseteq [{\widetilde{t}}_{1},{\widetilde{t}}_{3})\). For \(k=s\), by \({\widetilde{t}}_{s+1}=\pi =t_{s+1}\), \(|t_{s}-{\widetilde{t}}_{s}|\le \varepsilon\) and \(t_{s+1}-t_{s}\ge 2\varepsilon\) we have
Namely, \({\widetilde{t}}_{s}+\varepsilon \le {\widetilde{t}}_{s+1}\). Then (4.33) and (4.35) with \(i=s-1\) lead to \([{\widetilde{t}}_{s}-\varepsilon ,{\widetilde{t}}_{s}+\varepsilon )\subseteq [{\widetilde{t}}_{s-1},{\widetilde{t}}_{s+1})\). On the other hand, the direct observation on (4.33) with \(i=2,\ldots ,s-1\) gives us that (4.29) holds for \(k=3,\ldots ,s-1\). Summarizing what has been addressed above, (4.29) holds.
We next estimate \(\sum _{m\in {\mathbb {Z}}\setminus \{0\}}\Vert {\widehat{f}}-\widehat{{\widetilde{f}}}\Vert _{L^{2}([{\widetilde{t}}_{k}-\varepsilon +2m\pi ,{\widetilde{t}}_{k}+\varepsilon +2m\pi ))}^{2}\) for the two cases: \({\widetilde{t}}_{k}\le t_{k}\) and \({\widetilde{t}}_{k}> t_{k}\). By (1.15) and the definition of \({\widetilde{f}}\), for any \(\xi \in {\mathbb {R}}\) we have
If \({\widetilde{t}}_{k}\le t_{k}\), then it follows from (4.28) and the first equation in (4.36) that
And by (4.29) and the second equation in (4.36), we have
Correspondingly, (4.37) and (4.38) lead to
Direct observation on (4.39) gives us that we need to estimate
For any \(m\in {\mathbb {Z}}\setminus \{0\}\), define
From (1.1) and (4.40) we arrive at
For any fixed \(k\in \{2,\ldots ,s\}\), it follows from (2.14) that \({\widetilde{t}}_{k}\in (-\pi ,\pi )\), which together with (2.3) and \({\widetilde{t}}_{k}\le t_{k}\) leads to \(-\pi<{\widetilde{t}}_{k}\le t_{k}<\pi\). Then by (4.40) we have
Now combining (4.41) and (4.42) we have
Note that by (1.2) we have
where \(a\in (-1,1)\).
Then it follows from (4.43) and (4.44) that
Consequently, by (4.39) and (4.45) we have
If \({\widetilde{t}}_{k}> t_{k}\), then by the similar procedures for \({\widetilde{t}}_{k}\le t_{k}\), we can prove that (4.46) still holds for any fixed \(k\in \{2,\ldots ,s\}\). The proof is concluded. \(\square\)
Proof of Theorem 2.5
Note that \({\widetilde{t}}_{1}=-\pi\) and \({\widetilde{t}}_{s+1}=\pi\). Then by Lemma 4.2 (4.29), we have \(-\pi ={\widetilde{t}}_{1}<{\widetilde{t}}_{2}<\ldots<{\widetilde{t}}_{s}<{\widetilde{t}}_{s+1}=\pi\). Consequently,
Define \(Q_{1}:=\cup _{k=2}^{s}[{\widetilde{t}}_{k}-\varepsilon ,{\widetilde{t}}_{k}+\varepsilon )\) and
where \(\varepsilon \in (0,\frac{\mu _{0}}{2}]\). Incidentally, for (4.33), (4.34) and (4.35), \(Q_{2}\) is well defined. By (4.47), it is easy to check that
Next we prove that
for \(\xi \in Q_{2}+2l\pi\) with any \(l\in {\mathbb {Z}}\). For \(s\ge 2\), recall that \(\{{\widetilde{t}}_{k}\}_{k=2}^{s}\) is constructed via (2.14) such that \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon\). Then by \(|t_{2}-{\widetilde{t}}_{2}|\le \varepsilon\) and \(|t_{s}-{\widetilde{t}}_{s}|\le \varepsilon\) we have \({\widetilde{t}}_{2}-\varepsilon \le t_{2}\) and \(t_{s}\le {\widetilde{t}}_{s}+\varepsilon\), which together with \({\widetilde{t}}_{1}=t_{1}\) and \({\widetilde{t}}_{s+1}=t_{s+1}\) lead to
Now combining (4.51) and Lemma 4.2 (4.36) we have
That is, (4.50) holds for \(s=2\), or for \(s>2\) and \(\xi \in \Big ([{\widetilde{t}}_{1},{\widetilde{t}}_{2}-\varepsilon ) \cup [{\widetilde{t}}_{s}+\varepsilon ,{\widetilde{t}}_{s+1})\Big )+2l\pi\). By (4.48), to complete the proof of (4.50) we just need to prove it for \(s>2\) and
For \(k\in \{2,\ldots ,s-1\}\), it is easy to derive from \(|t_{k}-{\widetilde{t}}_{k}|\le \varepsilon\) and \(|t_{k+1}-{\widetilde{t}}_{k+1}|\le \varepsilon\) that
By (4.36), the restriction of \({\widehat{f}}\) on the interval \([t_k, t_{k+1})+2l\pi\) is
which together with (4.53) leads to
Using (4.36) again, the restriction of \(\widehat{{\widetilde{f}}}\) on \([{\widetilde{t}}_k, {\widetilde{t}}_{k+1})+2l\pi\) is
which together with \([{\widetilde{t}}_{k}+\varepsilon ,{\widetilde{t}}_{k+1}-\varepsilon )\subset [{\widetilde{t}}_k, {\widetilde{t}}_{k+1})\) leads to that
Then it follows from (4.54) and (4.55) that
Summarizing what has been addressed above, (4.50) holds.
Next we estimate \(\Vert {\widehat{f}}-\widehat{{\widetilde{f}}}\Vert _{L^{2}({\mathbb {R}})}^{2}\). By (4.49) and (4.50), it is easy to check that
where
Note that \(\{t_{k},{\widetilde{t}}_{k}\}\subset [{\widetilde{t}}_{k}-\varepsilon ,{\widetilde{t}}_{k}+\varepsilon )\) for \(k\in \{2,\ldots ,s\}\). Motivated by this, \(P_{1}\) is estimated for the two cases: \({\widetilde{t}}_{k}\le t_{k}\) and \({\widetilde{t}}_{k}> t_{k}\). If \({\widetilde{t}}_{k}\le t_{k}\), then it follows from (4.28) and the first equation in (4.36) that
On the other hand, by (4.29) and the second equation in (4.36) we have
which together with (4.59) leads to
Direct observation on (4.48) and (4.49) gives us that \([{\widetilde{t}}_{k}-\varepsilon ,{\widetilde{t}}_{k}+\varepsilon )\subset [-\pi ,\pi )\). Recall that \([{\widetilde{t}}_{k},t_{k})\subset [{\widetilde{t}}_{k}-\varepsilon ,{\widetilde{t}}_{k}+\varepsilon )\). Then \([{\widetilde{t}}_{k},t_{k})\subset [-\pi ,\pi )\). Clearly, by (1.2) with \(n=0\) we have
Consequently,
If \({\widetilde{t}}_{k}> t_{k}\), then by the similar procedures for \({\widetilde{t}}_{k}\le t_{k}\), (4.62) still holds. For the second term \(P_{2}\), it follows from Lemma 4.2 that
Now from (4.57), (4.62) and (4.63) we arrive at
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Li, Y., Huang, Y. & Zhou, C. Reconstruction of spline spectra-signals from generalized sinc function by finitely many samples. Banach J. Math. Anal. 15, 33 (2021). https://doi.org/10.1007/s43037-020-00116-4
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Keywords
- Generalized sinc function
- Shift-invariant space
- Spline-spectra signals
- Fourier samples
Mathematics Subject Classification
- 42C40
- 41A30
- 42A16