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2-D Stationary Wavelet Transform and 2-D Dual-Tree DWT for MRI Denoising

  • 0Center of Biotechnogy of Borj Cédria, Laboratory LMEEVED, Hammam-Lif, Tunisia.
Current Medical Imaging +

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July 8, 2025

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July 8, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel image denoising approach using Stationary Wavelet Transform (SWT 2-D) and Dual-Tree Discrete Wavelet Transform (DWT). The proposed method effectively removes noise from images, including MRIs, outperforming existing techniques in key metrics.

Area Of Science

  • Digital Image Processing
  • Medical Imaging
  • Signal Processing

Background

  • Image noise is an unavoidable artifact during acquisition, transmission, and processing.
  • Effective noise reduction is crucial for subsequent image analysis and interpretation, especially for Magnetic Resonance Images (MRIs).
  • Existing denoising methods have limitations in noise removal and detail preservation.

Purpose Of The Study

  • To propose and evaluate a novel image denoising approach for noisy images, including MRIs.
  • To enhance image quality by effectively removing noise while preserving important image features.
  • To compare the performance of the proposed method against established denoising techniques.

Main Methods

  • The approach utilizes a combination of Stationary Wavelet Transform (SWT 2-D) and 2-D Dual-Tree Discrete Wavelet Transform (DWT).
  • Noisy images are first transformed using 2-D Dual-Tree DWT to obtain wavelet coefficients.
  • These coefficients are then denoised using an SWT 2-D based technique, followed by inverse 2-D Dual-Tree DWT to reconstruct the denoised image.

Main Results

  • The proposed approach demonstrated superior performance compared to four other denoising techniques.
  • Quantitative evaluation using Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Normalized Mean Square Error (NMSE), and Feature Similarity (FSIM) confirmed its effectiveness.
  • The method achieved higher PSNR, SSIM, and FSIM values, along with lower NMSE, indicating better noise reduction and image quality.

Conclusions

  • The proposed SWT 2-D and 2-D Dual-Tree DWT based image denoising approach significantly outperforms existing methods.
  • It effectively eliminates noise from grayscale images and MRIs, with minimal distortion of image details, particularly at moderate noise levels (σ = 10, 20).
  • While noise reduction is substantial at higher noise levels (σ = 30, 40), some minor distortions may occur, highlighting a trade-off between noise removal and detail preservation.

Keywords:

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