Comparative Analysis of Bilateral Filter and its Variants for Magnetic Resonance Imaging

Kaur, Bobbinpreet; Dogra, Ayush; Goyal, Bhawna

doi:10.2174/1874440002013010021

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Namely, IQA methods evaluate the perceptual quality of digital images and support, among others, image enhancement [ 2 ], restoration [ 3 ], steganography [ 4 ], or denoising algorithms [ 5 ]. Further, IQA is also necessary in the benchmarking of many image processing or computer-vision algorithms [ 6 , 7 , 8 ]. In the literature, IQA is classified into two groups, i.e., subjective and objective IQA.…”

Section: Introductionmentioning

confidence: 99%

Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

Varga

2022

J. Imaging

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Digital images can be distorted or contaminated by noise in various steps of image acquisition, transmission, and storage. Thus, the research of such algorithms, which can evaluate the perceptual quality of digital images consistent with human quality judgement, is a hot topic in the literature. In this study, an image quality assessment (IQA) method is introduced that predicts the perceptual quality of a digital image by optimally combining several IQA metrics. To be more specific, an optimization problem is defined first using the weighted sum of a few IQA metrics. Subsequently, the optimal values of the weights are determined by minimizing the root mean square error between the predicted and ground-truth scores using the simulated annealing algorithm. The resulted optimization-based IQA metrics were assessed and compared to other state-of-the-art methods on four large, widely applied benchmark IQA databases. The numerical results empirically corroborate that the proposed approach is able to surpass other competing IQA methods.

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Section: Introductionmentioning

confidence: 99%

Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

Varga

2022

J. Imaging

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Design of Approximate Bilateral Filters for Image Denoising on FPGAs

et al. 2023

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This paper presents the hardware design of fast and low-cost denoising filters suitable to be exploited in the enabling technologies for Industry 5.0. A novel approximate computing strategy is introduced to reduce the computational complexity of the image denoising operation and to comply with real-time requirements. Firstly, it is demonstrated that the novel approximate approach can be helpfully exploited in the design of reconfigurable denoising filters able to reach image qualities as close as possible to the precise software counterparts. The reconfigurability leads to hardware architectures run-time adaptable to different levels of noise, whereas the adopted approximation strategy limits hardware resources and energy requirements. Quality tests, performed at various image and kernel sizes, and noise standard deviations, demonstrate that the approximate denoising approach presented here reaches PSNR and SSIM comparable with the precise denoise filtering. In comparison with state-of-the-art FPGA-based competitors, the novel filters reduce the resources requirements by up to 70%, achieve frame rates up to 35 times higher, and dissipate more than 45% lower power. When implemented within the XC7Z7020 FPGA device, a 5×5 filter designed as proposed here denoises 512×512 grayscale images using only 1689 LUTs, 2635 Flip-Flops and 32 DSPs. Moreover, it processes up to 926.8 frames per second, consumes just 63mW @ 244MHz and, with a noise standard deviation equal to 10, it achieves an average PSNR of ̴ 33dB with an average SSIM of ̴ 0.86.INDEX TERMS Approximate computing, Bilateral filtering, FPGA-based designs, Image denoising.

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Application of Bilateral Filters for Denoising Rician Noise in MRI Images

2023

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)

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Comparative Analysis of Bilateral Filter and its Variants for Magnetic Resonance Imaging

Cited by 3 publications

References 15 publications

Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

Full-Reference Image Quality Assessment Based on an Optimal Linear Combination of Quality Measures Selected by Simulated Annealing

Design of Approximate Bilateral Filters for Image Denoising on FPGAs

Application of Bilateral Filters for Denoising Rician Noise in MRI Images

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