• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1525-1536
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• 2021

Objective: To investigate the feasibility of cine three-dimensional (3D) balanced steady-state free precession (b-SSFP) imaging combined with a non-local means (NLM) algorithm for image denoising in evaluating cardiac function in children with repaired tetralogy of Fallot (rTOF). Materials and Methods: Thirty-five patients with rTOF (mean age, 12 years; range, 7-18 years) were enrolled to undergo cardiac cine image acquisition, including two-dimensional (2D) b-SSFP, 3D b-SSFP, and 3D b-SSFP combined with NLM. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) of the two ventricles were measured and indexed by body surface index. Acquisition time and image quality were recorded and compared among the three imaging sequences. Results: 3D b-SSFP with denoising vs. 2D b-SSFP had high correlation coefficients for EDV, ESV, SV, and EF of the left (0.959-0.991; p < 0.001) as well as right (0.755-0.965; p < 0.001) ventricular metrics. The image acquisition time ± standard deviation (SD) was 25.1 ± 2.4 seconds for 3D b-SSFP compared with 277.6 ± 0.7 seconds for 2D b-SSFP, indicating a significantly shorter time with the 3D than the 2D sequence (p < 0.001). Image quality score was better with 3D b-SSFP combined with denoising than with 3D b-SSFP (mean ± SD, 3.8 ± 0.6 vs. 3.5 ± 0.6; p = 0.005). Signal-to-noise ratios for blood and myocardium as well as contrast between blood and myocardium were higher for 3D b-SSFP combined with denoising than for 3D b-SSFP (p < 0.05 for all but septal myocardium). Conclusion: The 3D b-SSFP sequence can significantly reduce acquisition time compared to the 2D b-SSFP sequence for cine imaging in the evaluation of ventricular function in children with rTOF, and its quality can be further improved by combining it with an NLM denoising method.