GPU-based anisotropic diffusion algorithm for video image denoising

Fredj, Amira Hadj; Malek, Jihene

doi:10.1016/j.micpro.2017.08.003

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…The threshold was defined as the mean of gradient magnitude, and it was updated during the iterations of diffusion. Hadj Fredj and Malek [73] studied the oriented speckle reducing anisotropic diffusion (OSRAD) and tried to improve its computational efficiency so that the OSRAD can be applied in real-time denoising.…”

Section: Anisotropic Diffusion Filtering For Denoisingmentioning

confidence: 99%

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

et al. 2021

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Section: Anisotropic Diffusion Filtering For Denoisingmentioning

confidence: 99%

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

et al. 2021

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“…Hadj Fredj et al (2017) has suggested an anisotropic diffusion filter. Anisotropic diffusion may be employed for eliminating noise as digital images do not blur the edges.…”

Section: Literature Surveymentioning

confidence: 99%

A field programmable gate array-based biomedical noise reduction framework using advanced trilateral filter

Raja

Naveen

Palaniswamy

et al. 2021

Transactions of the Institute of Measurement and Control

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Filtering is one of the essential tools utilized to remove undesirable features in biomedical images. Most biomedical image denoising systems are used for clinical diagnosis. So, in this paper, we use the advanced trilateral filter in the field programmable gate array (FPGA) for removing noise in the biomedical image. Generally, the trilateral filter is used as an edge preserving smoothing filter. This advanced approach of trilateral filter gives the best noise diminution and enhances the image quality. This paper also proposes the hardware implementation of an efficient FPGA-based advanced trilateral filter on real time execution. In this manuscript, we intend to design and implement the FPGA architecture using an advanced trilateral filter. Biomedical images with different noises are used during implementation and compared with the existing bilateral and trilateral architecture to assess the proposed architecture performance. For evaluating the performance metrics of the proposed advanced trilateral filter on MATLAB platform, peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) are calculated for different biomedical images – such as brain (MRI), chest (x-ray) and lungs (CT) – with different noises – such as salt and pepper, Gaussian, Poisson and Speckle noises – compared with existing bilateral filter and trilateral filter, respectively. The proposed advanced trilateral filter implementations are checked on Virtex-6, Virtex-7 and Zynq FPGA development board using Verilog programming language in Xilinx ISE 14.5 design tools. The simulation outcomes display that the FPGA execution of the advanced trilateral filter contains better noise removal efficiency in biomedical images compared with the existing bilateral and trilateral filter.

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