Regularization-based multi-frame super-resolution: A systematic review

Khattab, Mahmoud M.; Zeki, Akram M.; Alwan, Ali A.; Badawy, Ahmed Said

doi:10.1016/j.jksuci.2018.11.010

Cited by 16 publications

(13 citation statements)

References 35 publications

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“…(7) ). Using data-driven regularization that depends on image content can have benefits, such as edge-preservation ( Khattab et al, 2020 ). Forth, the multi-stack rotation SRR model suffers from signal over- and undershoot problems at contrast-rich regions due to regularization, as more thoroughly described by ( Shilling et al, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

et al. 2021

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“…Second, we used the identity matrix as regularization matrix. Using image-dependent regularization can have benefits, such as edge-preservation [51]. In addition, our analysis does not include motion and eddy current correction in the SRR model, while perfect registration in SRR is of importance to obtain non-blurry high-resolution results.…”

Section: Discussionmentioning

confidence: 99%

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Vis

Nilsson

Westin

et al. 2021

Preprint

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Diffusion MRI (dMRI) is a useful probe of tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution and/or high diffusion encoding strengths are used. Low SNR leads not only to poor precision but also poor accuracy of the diffusion-weighted signal, as the rectified noise floor gives rise to a positive signal bias. Recently, super-resolution techniques have been proposed for signal acquisition at a low spatial resolution but high SNR, whereafter a higher spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using strictly controlled phantom experiments, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. Moreover, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find that a gain in precision from super-resolution reconstruction (SRR) is substantial only when some spatial resolution is sacrificed. We also demonstrated the value of SRR in the challenging combination of high resolution and spherical b-tensor encoding at ultrahigh b-values—a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. We conclude that SRR is most valuable in low-SNR conditions, where it can suppress rectified noise floor effects and recover signal with high accuracy. The in vivo application showcases a vastly superior image contrast when using SRR compared to conventional imaging, facilitating investigations of brain tissue that would otherwise have prohibitively low SNR, resolution or required non-conventional MRI hardware.

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“…ere are some degrading influences in the practical sampling process including atmospheric instability, the motion of objects, blur effect, and downsampling equipment [12]. Consequently, the choice of a suitable degradation model is the primary key to reconstruct the SR image as shown in Figure 1 which is the core kernel of the image reconstruction process [31].…”

Section: Image Observation Modelmentioning

confidence: 99%

“…To perform the processes of quantitative analysis, evaluation, and comparison for the reconstruction performance, we generate synthetic data and calculate the peak-signal-to-noise ratio (PSNR) indicator and the structural similarity (SSIM) indicator of our estimated HR image with regard to a ground-truth image [8,26,31,38,39]. erefore, the PSNR measurement is calculated from the mean square error (MSE), where the MSE reflects the average error among the reconstructed SR image and the original HR image.…”

Section: Performance Analysismentioning

confidence: 99%

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Khattab

Zeki

Alwan

et al. 2021

Journal of Electrical and Computer Engineering

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The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.

show abstract

Regularization-based multi-frame super-resolution: A systematic review

Cited by 16 publications

References 35 publications

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

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