Hong Zheng1, Kun Zeng1, Di Guo2, Jiaxi Ying1, Yu Yang1, Xi Peng3, Zhong Chen1, and Xiaobo Qu1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2Xiamen University of Technology, Xiamen, China, 3Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Synopsis
Since
magnetic resonance imaging (MRI) can offer images of an object with different
contrasts, e.g., T1-weighted or T2-weighted, the shared information between
inter-contrast images can be used to benefit super-resolution. Multi-contrast
images are assumed to possess the same gradient direction in a local pattern.
We proposed to establish a relation model of gradient value between different
contrast images, to restore a high-resolution image from its input
low-resolution version. The similarity of image patches is employed to estimate
intensity parameters, leading a more accurate reconstructed image. Then,
iterative back-projection filter is applied to the reconstructed image to
further increase image quality. The reconstructed edges are more consistent to
the original high-resolution image, indicated with higher PSNR and SSIM than
the compared methods.
Purpose
In
magnetic resonance imaging (MRI), low-resolution images may be acquired and
their resolutions can be improved by applying super-resolution methods. MRI usually
offer images of an object with different contrasts, e.g. T1-weighted or
T2-weighted imaging (Fig. 1). Thus the shared information between
inter-contrast images can be used to benefit super-resolution [1]. In this
paper, we first establish a gradient relation model among different contrast
images and then reconstruct the super-resolved edges by incorporating the
gradient information from another contrast high-resolution image. Brain image
edges are further improved with similar patches estimation and iterative
back-projection filter. The reconstructed edges are more consistent to the
original high-resolution image, indicated with higher peak signal-to-noise
ratio (PSNR) and structure similarity index (SSIM) [2] than the compared
methods.Methods
Multi-contrast images
$$${\bf{\tilde X}}$$$
and
$$${\bf{\tilde R}}$$$
are assumed to possess the same gradient
direction in a local pattern. We proposed to establish a relation model of
gradient value between different contrast images (Fig. 2), to restore a
high-resolution image from its input low-resolution version. Assuming that an adjustment parameter
$$${\delta
_{i,j}}$$$
is added to the
(i, j) pixel
$$${X_{i,j}}$$$
of a pre-interpolated image
$$${\bf{X}}$$$
, our model can be expressed as
$$\begin{array}{l}
\mathop {\min
}\limits_{{\delta _{i,j}}} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\{ {\kern 1pt} [g({X_{i,j}}
+ {\delta _{i,j}}) - g({X_{i,j}})] - \lambda {\kern 1pt} {\kern 1pt}
[g({{\tilde R}_{i,j}}) - g({R_{i,j}})]\} ^2}\\
{\kern 1pt} {\kern 1pt}
{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern
1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt}
{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern
1pt} {\kern 1pt} {\kern 1pt} + {\{ [{g_
\bot }({X_{i,j}} + {\delta _{i,j}}) - {g_ \bot }({X_{i,j}})] - {\lambda _ \bot
}{\kern 1pt} {\kern 1pt} [{g_ \bot }({{\tilde R}_{i,j}}) - {g_ \bot
}({R_{i,j}})]\} ^2}
\end{array} (1)$$ where
$$$g\left( \cdot \right)$$$
is a second
order gradient function,
$$${\tilde
X_{i,j}}$$$ is the pixel of $$${\bf{\tilde X}}$$$, and $$${\tilde R_{i,j}}$$$ is the pixel of $$${\bf{\tilde R}}$$$. $$${R_{i,j}}$$$ is the pixel of $$${\bf{R}}$$$, which is the pre-interpolated image of LR version
of
$$${\bf{\tilde R}}$$$. The
$$$\lambda $$$ and $$${\lambda _ \bot }$$$ are parameters
that adjusting intensity in gradient direction and the edge direction. In here,
their values are estimated as SRGR model [3] as
$$\begin{array}{l}
\lambda = ({X_{i + 1,j - 1}} - {X_{i - 1,j +
1}})/{\kern 1pt} {\kern 1pt} {\kern 1pt} ({{\tilde R}_{i + 1,j - 1}} - {{\tilde
R}_{i - 1,j + 1}}){\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt}
{\kern 1pt} {\kern 1pt} \\
{\lambda _ \bot } = ({X_{i -
1,j - 1}} - {X_{i + 1,j + 1}})/{\kern 1pt} {\kern 1pt} {\kern 1pt} ({{\tilde
R}_{i - 1,j - 1}} - {{\tilde R}_{i + 1,j + 1}}){\kern 1pt} .{\kern 1pt} {\kern
1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt}
{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern
1pt}
\end{array} (2)$$ The
proposed model finds
refinements on pixels to minimize the gradient difference between the HR images
and HR reference images. The similarity of image patches [1, 4-5] is
employed to estimate intensity parameters, leading a more accurate
reconstructed image. Then, iterative back-projection (IBP) filter [6] is
applied to the reconstructed image to further increase image quality.Results
Experimental
results on synthetic MRI images are presented in Fig. 3. The new approach,
whether without IBP or with IBP, achieves higher visual quality and higher
objective quality criteria (PSNR and SSIM [2]) than the compared
state-of-the-art super-resolution approaches.Conclusions
The
gradient information of the multi-contrast MRI images is very useful. With a
proper relation model, the proposed method enhances image edges in MRI image
super-resolution.Acknowledgements
This
work was supported by the National Key R&D Program of China
(2017YFC0108700), National Natural Science Foundation of China (61571380, U1632274 and 61302174), Natural Science Foundation of Fujian Province of China
(2016J05205). The correspondence
should be sent to Dr. Xiaobo Qu (Email: quxiaobo@xmu.edu.cn).References
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