Abstract
We propose a variational image registration method for a pair of images with different resolutions. Traditional image registration methods match images assuming that the resolutions of the reference and target images are homogeneous. For the registration of inhomogeneous-resolution image pairs, we first introduce a resolution-conversion method to harmonise the resolution of a pair of images using the rational-order pyramid transform.Then, we develop a variational method for image registration using this resolution-conversion method.
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Appendix: Proofs of Theorems 1, 2 and 3
Appendix: Proofs of Theorems 1, 2 and 3
Using the matrix expression of downsampling for vectors \(\varvec{S}_q=\varvec{I}_n\otimes \varvec{e}_1^q\), where \(\varvec{e}_1^q=(1,0,\cdots ,0)^\top \in \mathbf{R}^q\), the two-dimensional downsampling is expressed as \(\varvec{G}=\varvec{S}_{q}\varvec{F}\varvec{S}_{q}^\top \). This expression derives the following lemma.
Lemma 1
Assuming that the domain of images is \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_i^\top \}_{i,j=0}^{n-1}\), the range of images downsampled by factor q is \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_j^\top \}_{i,j=0}^{\frac{1}{q}n-1}\).
The \(2p+1\)-dimensional diagonal matrix \(\varvec{N}_p=\left( \left( n_{|i-j|} \right) \right) \), where \(n_k=\frac{p-k}{p}\) for \(0\le p \le k\). is expressed as \(\varvec{N}_p=\sum _{k=0}^p a_k\varvec{D}_{n}^k,\) where \(\varvec{D}_{n}^0=\varvec{I}_n\), for an appropriate collection of coefficients \(\{a_k\}_{k=0}^p\). The linear interpolation for the two-dimensional image \(\varvec{F}\) is \(\varvec{N}_p\varvec{S}_p\varvec{F}(\varvec{N}_p\varvec{S}_p)^\top =\varvec{N}_p\varvec{S}_p\varvec{F}\varvec{S}_p^\top \varvec{N}_p^\top \). This expression implies the following lemma.
Lemma 2
Assuming that the domain of images is \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_j^\top \}_{i,j=0}^{n-1}\) the range of images interpolated by order p is \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_j^\top \}_{i,j=0}^{pn-1}\).
Furthermore, the pyramid transform of factor q is \(\frac{1}{q^2}\varvec{S}_q\varvec{N}_q\varvec{F}\varvec{N}_q^\top \varvec{S}_q^\top \), since the pyramid transform is achieved by downsampling after shift-invariant smoothing. This expression of the pyramid transform implies the following lemma.
Lemma 3
With the Neumann boundary condition, the pyramid transform of order q is a linear transform from \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_j^\top \}_{i,j=0}^{n-1}\) to \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_i^\top \}_{i,j=0}^{\frac{1}{q}n-1}\), assuming \(n=kq\).
Moreover, the matrix form of the \(q\mathrm{{/}}p\)-pyramid transform for the two-dimensional image \(\varvec{F}\) is
This expression implies the following lemma.
Lemma 4
With the Neumann boundary condition, the \(q\mathrm{{/}}p\) pyramid transform is a linear transform from \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_j^\top \}_{i,j=0}^{n-1}\) to \(\mathcal {L}\{\varvec{\varphi }_i\varvec{\varphi }_j^\top \}_{i,j=0}^{\frac{p}{q} n -1}\).
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Hosoya, K., Imiya, A. (2019). Variational Image Registration for Inhomogeneous-Resolution Pairs. In: Lellmann, J., Burger, M., Modersitzki, J. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2019. Lecture Notes in Computer Science(), vol 11603. Springer, Cham. https://doi.org/10.1007/978-3-030-22368-7_22
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DOI: https://doi.org/10.1007/978-3-030-22368-7_22
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