Medical image denoising via optimal implementation of non-local means on hybrid parallel architecture

Nguyen, Tuan-Anh; Nakib, Amir; Nguyen, Huy-Nam

doi:10.1016/j.cmpb.2016.02.002

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…A slightly higher speedup to the one obtained by Marques and Pardo [24] was achieved parallelizing the filter with MPI in a distributed memory system with 1024 cores [27]. A more complex implementation of an improved NLM filter on hybrid parallel architectures, combined MPI with P-threads and MPI with CUDA to achieve a high speedup [28]. Finally, two implementations of the filter on Intel Xeon Phi platforms were proposed using OpenMP [29], [30] and OpenCL [29].…”

Section: Previous Parallelization Approaches For Non Local Means Filtermentioning

confidence: 98%

DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

Calderón

Castro

Zurnbado

2018

2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)

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This paper presents a novel computational optimization of the deceived non local means filter using moving average and symmetric weighting. The proposed optimization is compared with different approaches that reduce the computational cost of the deceived non local means filter. Furthermore, the impact of parallelizing different optimization approaches is assessed by evaluating the execution time and scalability in Xeon Phi KNL architecture. The proposed optimization for the sequential implementation achieved a 90x speedup, while its parallelized implementation yielded a speedup of up to 1662x.

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Section: Previous Parallelization Approaches For Non Local Means Filtermentioning

confidence: 98%

DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

Calderón

Castro

Zurnbado

2018

2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)

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“…Salvatore Cuomo et al [20] proposed a full 3D non-local mean parallel method based on multi-GPUs, which had high applicability and scalability. Tuan-Anh Nguyen et al [21] implemented a non-local mean denoising filter based on multi-GPUs, which reduced shared memory access and improved denoising speed. Zou Guoliang et al [22] used OpenMP on a multicore computer to realize the parallel simulation of the image processing process of ocean wave sample data set by weighted mean filtering, and the acceleration ratio was up to 24.29 times.…”

Section: Related Research Workmentioning

confidence: 99%

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

et al. 2021

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The image will be contaminated by noise during the imaging process, which severely degrades the image quality. It is necessary to filter the collected image. With the increasing amount of image data, the traditional single-processor or multiprocessor computing equipment has been unable to meet the requirements of real-time data processing. In this paper, the computational model of weighted mean filtering and the characteristics of high performance computer architecture are studied. An efficient hierarchical image weighted mean filtering parallel algorithm for Open Computing Language (OpenCL) is designed and implemented, which can fully express the parallelism of the computing model. The parallel algorithm takes full account of the characteristics of image discrete convolution computing and the multilayer logic architecture of high performance computer, deeply excavates the parallelism of the computing platform and computing model, and realizes the efficient task mapping from computing model to computing resources. The model is implemented in parallel with the two levels of work-group and workitem. The experimental results show that compared with the serial algorithm based on CPU, the parallel algorithm based on Open Multi-Processing (OpenMP) and the parallel algorithm based on Compute Unified Device Architecture (CUDA), the parallel algorithm of weighted mean filtering achieves 20.88 times, 18.52 times and 1.26 times acceleration ratio on the NVIDIA GPU computing platform based on OpenCL architecture, respectively. It realizes better computing performance and runs on different Graphic Processing Unit (GPU) computing platforms, and has good portability and scalability.INDEX TERMS weighted mean filtering; Gaussian noise; Graphic Processing Unit (GPU); Open Computing Language (OpenCL); parallel algorithm.

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“…Nguyena et al [61] presented a hybrid parallelization scheme with the aim of accelerating the NL-Means filter algorithm. In their approach, the authors divided the input 3D MRI volume into sub-volumes in order to reduce the search region at the boundary zone.…”

Section: Image Filteringmentioning

confidence: 99%

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Gulo

Sementille

Tavares

2017

J Real-Time Image Proc

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Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

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Medical image denoising via optimal implementation of non-local means on hybrid parallel architecture

Cited by 8 publications

References 16 publications

DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

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