A new hybrid image denoising algorithm using adaptive and modified decision-based filters for enhanced image quality

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 15, 2025

Denoising is one of the most important processes in digital image processing to recover visual quality and structural integrity images. Traditional methods often suffer from limitations like computational complexity, over-smoothing, inability preserve critical details, particularly edges. This paper introduces a hybrid denoising algorithm combining Adaptive Median Filter (AMF) Modified Decision-Based (MDBMF) address these challenges. The AMF adjusts window sizes dynamically precisely detect noisy pixels, MDBMF selectively recovers corrupted pixels without affecting intact regions, effectively reducing noise while preserving subjective analysis supplemented with objective analyses which proves that approach performance considerably outperforms existing state-of-the-art methods. test conducted on nine benchmark images standard medical dataset, namely, Chest Liver different densities range 10 90%. Quantitative evaluations PSNR, MSE, IEF, SSIM, FOM VIF clearly show superiority when compared approaches. improvement PSNR was up 2.34 dB, IEF more than 20%, MSE 15% over other BPDF, AT2FF, SVMMF. Improvement values SSIM 0.07, confirms improved similarity. Furthermore, metrics demonstrate remarkable approach: both exceeded all techniques evaluated, reaching 0.68 0.61, respectively.

Language: Английский

Evaluation of Speckle Noise Reduction Filters and Machine Learning Algorithms for Ultrasound Images

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 81293 - 81312

Published: Jan. 1, 2024

Language: Английский

Hybrid Despeckling for Ultrasound Images Using Sticks Filter and Fourth-Order PDE for Enhanced Diagnostic Precision

Deleted Journal, Journal Year: 2024, Volume and Issue: 3(5), P. 1 - 8

Published: Nov. 30, 2024

Speckle noise in ultrasound imaging poses significant challenges by degrading image quality and affecting diagnostic precision. This study evaluates compares the performance of established despeckling algorithms, including Lee, Kuan, Frost, Non-Local Means, PMAD filters, as well advanced techniques such Fourth-Order Partial Differential Equations (PDEs) a novel hybrid method combining Sticks filters with PDE. Quantitative assessment was performed using metrics Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Equivalent Number Looks (ENL), Structural Similarity Index (SSI), Signal-to-Mean Power (SMPI), computational efficiency. Among evaluated methods, Lee filter achieved highest PSNR 25.05 dB, demonstrating effective suppression while preserving details image. The combination PDE ENL 0.0331, indicating superior smoothing homogeneous regions enhanced contrast. While exhibited speckle minimal MSE 886.49, it introduced slight blurring, compromising structural details. Visual inspections revealed that approach delivered exceptional edge preservation contrast enhancement, outperforming other clinical scenarios thyroid nodule analysis. results demonstrate proposed addresses critical trade-offs between detail preservation, offering robust framework to improve utility images. Future research could explore optimizing these algorithms for real-time applications, enabling broader adoption.

Language: Английский