Online multi-frame super-resolution of image sequences

Xu, Jieping; Liang, Yonghui; Liu, Jin; Huang, Zongfu; Li, Xuewen

doi:10.1186/s13640-018-0376-5

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Although the frequency domain-based methods have low computational complexity, they behave extremely sensitive to model errors and have limited ability to integrate a priori knowledge as regularization. The majority of the iterative MISR approaches solve the problem in the spatial domain based on the maximum likelihood (ML), the maximum a posteriori (MAP), and the projection onto convex sets (POCS) [23,25,27,[29][30][31][32][33]. Most of the work focuses on the reconstruction accuracy and only few concerns the performance in computation time.…”

Section: Optimization-based Iterative Methodsmentioning

confidence: 99%

FL-MISR: fast large-scale multi-image super-resolution for computed tomography based on multi-GPU acceleration

Sun

Tran

Guhathakurta

et al. 2021

J Real-Time Image Proc

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Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR in practice. In this work, we propose a distributed optimization framework based on data parallelism for fast large-scale MISR using multi-GPU acceleration named FL-MISR. The scaled conjugate gradient (SCG) algorithm is applied to the distributed subfunctions and the local SCG variables are communicated to synchronize the convergence rate over multi-GPU systems towards a consistent convergence. Furthermore, an inner-outer border exchange scheme is performed to obviate the border effect between neighboring GPUs. The proposed FL-MISR is applied to the computed tomography (CT) system by super-resolving the projections acquired by subpixel detector shift. The SR reconstruction is performed on the fly during the CT acquisition such that no additional computation time is introduced. FL-MISR is extensively evaluated from different aspects and experimental results demonstrate that FL-MISR effectively improves the spatial resolution of CT systems in modulation transfer function (MTF) and visual perception. Comparing to a multi-core CPU implementation, FL-MISR achieves a more than 50$$\times$$ × speedup on an off-the-shelf 4-GPU system.

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Section: Optimization-based Iterative Methodsmentioning

confidence: 99%

FL-MISR: fast large-scale multi-image super-resolution for computed tomography based on multi-GPU acceleration

Sun

Tran

Guhathakurta

et al. 2021

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…Although the frequency domain based methods have low computational complexity, they behave extremely sensitive to model errors and have limited ability to integrate a priori knowledge as regularization. The majority of the iterative MISR approaches solve the problem in the spatial domain based on the maximum likelihood (ML), the maximum a posteriori (MAP), and the projection onto convex sets (POCS) [23,25,27,[29][30][31][32][33].…”

Section: Optimization-based Iterative Methodsmentioning

confidence: 99%

FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration

Sun¹,

Tran²,

Guhathakurta³

et al. 2021

Preprint

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Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR methods in practice. In this work, we propose a distributed optimization framework based on data parallelism for fast large-scale MISR which supports multi-GPU acceleration, named FL-MISR. Inter-GPU communication for the exchange of local variables and overlapped regions is enabled to impose a consensus convergence of the distributed task allocated to each GPU node. We have seamlessly integrated FL-MISR into the computed tomography (CT) imaging system by superresolving multiple projections of the same view acquired by subpixel detector shift. The SR reconstruction is performed on the fly during the CT acquisition such that no additional computation time is introduced. We evaluated FL-MISR quantitatively and qualitatively on multiple objects including aluminium cylindrical phantoms, QRM bar pattern phantoms, and concrete joints. Experiments show that FL-MISR can effectively improve the spatial resolution of CT systems in modulation transfer function (MTF) and visual perception. Besides, comparing to a multi-core CPU implementation, FL-MISR achieves a more than 50× speedup on an off-the-shelf 4-GPU system.

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“…Recovering more resolute scenes from their degraded versions has been a long-standing challenge that attracts the attention of most scholars. One approach to address the challenge is called MSR, which uses multiple frames of degraded images to reconstruct a high-resolution image [29][30][31]. Laghrib et al applied a non-local form of the bilateral total variation that takes into consideration complex spatial interactions within images [26].…”

Section: Multiframe Super-resolution (Msr)mentioning

confidence: 99%

“…Recovering more resolute scenes from their degraded versions has been a long‐standing challenge that attracts the attention of most scholars. One approach to address the challenge is called MSR, which uses multiple frames of degraded images to reconstruct a high‐resolution image [29–31]. Laghrib et al .…”

Section: Augmenting Pvr Systemsmentioning

confidence: 99%

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

et al. 2020

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Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate four stages: image acquisition, pre-processing, feature extraction, and decision. The first two stages determine accuracy of the final identification results. Focusing on the pre-processing component, we discovered that the available approaches fail to generate more informative vein patterns by simultaneously suppressing noise and blur, and also by recovering semantically useful features (edges, contours, and lines) from the acquired images. This weakness calls for sophisticated acquisition devices that make PVR systems costly. In this work, we have proposed multiframe super-resolution (MSR) as a pre-processing stage to improve performance of the traditional PVR systems. MSR exploits information from multiple images of the same scene to reconstruct a high-resolution image. This technique signals the possibility of using inexpensive low-resolution imaging devices demanded by the traditional PVR systems. Experiments show that our method outperforms most classical methods.

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Online multi-frame super-resolution of image sequences

Cited by 5 publications

References 22 publications

FL-MISR: fast large-scale multi-image super-resolution for computed tomography based on multi-GPU acceleration

FL-MISR: fast large-scale multi-image super-resolution for computed tomography based on multi-GPU acceleration

FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

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