Multi-frame super-resolution reconstruction based on mixed Poisson–Gaussian noise

Sun, Kaicong; Tran, Tu Anh; Krawtschenko, Roman; Simon, Sven

doi:10.1016/j.image.2019.115736

Cited by 12 publications

(17 citation statements)

References 44 publications

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“…The calculation of the system matrix i is thoroughly described in our previous work [26]. For an upscaling of 2 × with half pixel detector shift and a 3 × 3 Gaussian blur for B i , a 12-row block area in the HR grid is required as the overlapped region between neighboring GPUs.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Different from the deep learning-based SR methods, optimization-based MISR algorithms [23][24][25][26][27] reconstruct the latent high-resolution (HR) image explicitly based on the real acquisitions but not the training datasets. Nowadays, due to the technological development of sensor manufacturing, sensors or detectors with large resolutions such as 8, 16 Mpixels or even higher are employed in applications such as medical imaging and industrial inspection.…”

Section: Introductionmentioning

confidence: 99%

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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: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The calculation of the system matrix A i is thoroughly described in our previous work [26]. For an upscaling of 2× with half pixel detector shift and a 3 × 3 Gaussian blur B i , a 12-row block area in the HR grid was set as the overlapped region between two neighboring GPUs.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…It is run on Intel i3, 2 GHz PC with 8 GB RAM and Windows 10, 64-bit operating system. We have compared the results of the proposed method with competing algorithms such as MPGSR [5], NLPCA [12], PNLW (Poisson Non-local Wiener estimator) [29], TV-MAP [30], PURELET [26], and Un-modified wavelet method (UW). For better comparison, we have provided results of proposed method with traditional way of decomposition is termed as UW.…”

Section: Resultsmentioning

confidence: 99%

Mixed Poisson Gaussian noise reduction in fluorescence microscopy images using modified structure of wavelet transform

Rasal

Veerakumar

Subudhi

et al. 2020

IET Image Processing

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Fluorescence microscopy is an important investigation tool of discoveries in the field of biological sciences where the imaging phenomena are limited by the noise. This paper introduces the integration of biorthogonal wavelet filters along with mixed Poisson-Gaussian unbiased risk estimate (MPGURE) based subband adaptive thresholding function for the restoration of low photon count microscopy images. The proposed algorithm consists of four steps. In the first step, variance stabilization transform along with a multi-scale Wiener filtering approach is used to filter out the noise and blurring effect. In the second step, deconvolved images are further decomposed by the biorthogonal wavelet filters. The modified wavelet subband structure is used for the identification of noisy and noise-free subbands. In the next stage, different noisy coefficients are thresholded using the MPGURE-based thresholding operation. The different thresholded images are combined along with different optimum coefficients. Finally, inverse variance stabilization transformation is applied to obtain the final restored output. Performance of the proposed algorithm is tested on 14 different benchmark image data sets with performance evaluation measures like signal-to-noise ratio, peak signal-to-noise ratio, mean structural similarity index measur, and correlation coefficient. Simulation results of the proposed algorithm claim better results than other state-of-the-art techniques.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Multi-frame super-resolution reconstruction based on mixed Poisson–Gaussian noise

Cited by 12 publications

References 44 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

Mixed Poisson Gaussian noise reduction in fluorescence microscopy images using modified structure of wavelet transform

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