Medical Image Denoising Using Dual Tree Complex Thresholding Wavelet Transform and Wiener Filter
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Naimi, Hilal (Author) |
| مؤلفين آخرين: | Mitiche, Lahcene (Co-Author), Adamou-Mitiche, Amel Baha Houda (Co-Author) |
| المجلد/العدد: | مج27, ع1 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2015
|
| الصفحات: | 40 - 45 |
| DOI: |
10.33948/0584-027-001-004 |
| ISSN: |
1319-1578 |
| رقم MD: | 973497 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Discrete Wavelet Transform | Stationary Wavelet Trans Form | Wavelet Thresholding | Dual Tree Complex Wavelet Transform | Wiener Filter
|
| رابط المحتوى: |
| المستخلص: |
Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage with the Wiener filter technique (where either hard or soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used). The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) with Wiener filter have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (Stationary Wavelet Transform). We used the SSIM (Structural Similarity Index Measure) along with PSNR (Peak Signal to Noise Ratio) and SSIM map to assess the quality of denoised images |
|---|---|
| ISSN: |
1319-1578 |
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