BM3D image denoising using heterogeneous computing platforms

Sarjanoja, Sampsa; Boutellier, Jani; Hannuksela, Jari

doi:10.1109/dasip.2015.7367257

Cited by 18 publications

(17 citation statements)

References 19 publications

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“…The block-matching procedure was accelerated by an efficient data reuse method applied to the whole image, while the 3D transformation was accelerated by utilizing a fast software FFTW library. Later on, the work of [8] proposed the first open-source GPU-based implementation of the BM3D algorithm by using both the OpenCL and CUDA frameworks. The final design achieved 7.5× speed up compared to the CPU-based design of [7].…”

Section: Introductionmentioning

confidence: 99%

“…Very recently, the study of [9] presented a highly efficient GPU-based software accelerator implemented in CUDA. The authors proposed a more fine-grained partition of the algorithm compared to [8] and optimized data caching and sharing schemes for block-matching, which removed many redundant computations and thus significantly improved the image processing speed. In [10], the authors targeted an GPU implementation for VBM3D and focused on reducing the use of external memory bandwidth, which is realized by regrouping all filtering operations, including fetching the patch data, the data transpositions, the 1D filtering and the thresholding, into one kernel without requiring intermediate bufffer for patches caching.…”

Section: Introductionmentioning

confidence: 99%

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An FPGA-Based Hardware Accelerator for Real-Time Block-Matching and 3D Filtering

Wang

Jia

2020

IEEE Access

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Block-matching and 3D filtering (BM3D) denoising algorithm has been employed in many application fields because of its superior image processing quality. Due to the huge computational workload, real-time implementation of this algorithm is very challenging. Recently, studies on accelerating the BM3D algorithm on GPU have presented impressive speed up over CPU-based implementations. However, GPU devices are generally inefficient in energy dissipation and, thus, are not suitable for embedded application scenarios. In this paper, we propose a dedicated hardware accelerator design to efficiently boost the BM3D algorithm with reduced power consumption on FPGA device. The proposed design is based on a deeply pipelined OpenCL kernel architecture that can efficiently speed up the compute-intensive procedures of the denoising algorithm by exploiting the intrinsic parallelism and maximizing data reuse. The final design was implemented on Intel's Arria-10 GX1150 FPGA, and achieved an average 1.2× performance boost and an outstanding 8.3× reduction in energy dissipation when compared to a state-of-the-art GPU-based software design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An FPGA-Based Hardware Accelerator for Real-Time Block-Matching and 3D Filtering

Wang

Jia

2020

IEEE Access

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“…For a typically sized volume, the filtering speed requires further improvement in order for it to be useful in most typical MC simulations (10 6 to 10 8 photons). Although the GPU-BM3D filters 39,40 reported excellent speed, they are designed for filtering two-dimensional (2-D) images and are not suited for 3-D denoising. As far as we know, there is no publication on GPU-BM4D filters.…”

Section: Introductionmentioning

confidence: 99%

Graphics processing units-accelerated adaptive nonlocal means filter for denoising three-dimensional Monte Carlo photon transport simulations

Yuan

Dogan

et al. 2018

J. Biomed. Opt.

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The Monte Carlo (MC) method is widely recognized as the gold standard for modeling light propagation inside turbid media. Due to the stochastic nature of this method, MC simulations suffer from inherent stochastic noise. Launching large numbers of photons can reduce noise but results in significantly greater computation times, even with graphics processing units (GPU)-based acceleration. We develop a GPU-accelerated adaptive nonlocal means (ANLM) filter to denoise MC simulation outputs. This filter can effectively suppress the spatially varying stochastic noise present in low-photon MC simulations and improve the image signal-to-noise ratio (SNR) by over 5 dB. This is equivalent to the SNR improvement of running nearly 3.5× more photons. We validate this denoising approach using both homogeneous and heterogeneous domains at various photon counts. The ability to preserve rapid optical fluence changes is also demonstrated using domains with inclusions. We demonstrate that this GPU-ANLM filter can shorten simulation runtimes in most photon counts and domain settings even combined with our highly accelerated GPU MC simulations. We also compare this GPU-ANLM filter with the CPU version and report a threefold to fourfold speedup. The developed GPU-ANLM filter not only can enhance three-dimensional MC photon simulation results but also be a valuable tool for noise reduction in other volumetric images such as MRI and CT scans.

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“…For all details of this algorithm realization, interested readers can refer to [16,21,22], while software realization is available at (http://www.cs.tut.fi/~foi/GCF-BM3D/).…”

Section: Algorithm Bm3d Filtermentioning

confidence: 99%

“…The BM3D algorithm consists of the coarse and fine algorithm runs [16,21,22] (http://www.cs.tut.fi/~foi/ GCF-BM3D/). Here, the BM3D algorithm is briefly summarized.…”

Section: Bm3dmentioning

confidence: 99%

BM3D filter in salt-and-pepper noise removal

Djurović

2016

J Image Video Proc.

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There is a significant recent advance in filtering of the salt-and-pepper noise for digital images. However, almost all recent schemes for filtering of this type of noise are not taking into an account the shape of objects (in particular edges) in images. We have applied the block-matching and 3D filtering (BM3D) scheme in order to refine the output of the decision-based/adaptive median techniques. Obtained results are excellent, surpassing current state-of-the-art for about 2 dB for both grayscale and color images.

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BM3D image denoising using heterogeneous computing platforms

Cited by 18 publications

References 19 publications

An FPGA-Based Hardware Accelerator for Real-Time Block-Matching and 3D Filtering

An FPGA-Based Hardware Accelerator for Real-Time Block-Matching and 3D Filtering

Graphics processing units-accelerated adaptive nonlocal means filter for denoising three-dimensional Monte Carlo photon transport simulations

BM3D filter in salt-and-pepper noise removal

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