Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
(1) Background: Due to its imaging principle, OCT generates images laden with significant speckle noise. The quality of OCT images is a crucial factor influencing diagnostic effectiveness, highlighting the importance of OCT image denoising. (2) Methods: The OCT image undergoes a Discrete Wavelet Tra...
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2025-06-01
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| author | Fangfang Li Qizhou Wu Bei Jia Zhicheng Yang |
| author_facet | Fangfang Li Qizhou Wu Bei Jia Zhicheng Yang |
| author_sort | Fangfang Li |
| collection | DOAJ |
| description | (1) Background: Due to its imaging principle, OCT generates images laden with significant speckle noise. The quality of OCT images is a crucial factor influencing diagnostic effectiveness, highlighting the importance of OCT image denoising. (2) Methods: The OCT image undergoes a Discrete Wavelet Transform (DWT) to decompose it into multiple scales, isolating high-frequency wavelet coefficients that encapsulate fine texture details. These high-frequency coefficients are further processed using a Shift-Invariant Wavelet Transform (SWT) to generate an additional set of coefficients, ensuring an enhanced feature preservation and reduced artifacts. Both the original DWT high-frequency coefficients and their SWT-transformed counterparts are independently denoised using a Deep Neural Convolutional Network (DnCNN). This dual-pathway approach leverages the complementary strengths of both transform domains to suppress noise effectively. The denoised outputs from the two pathways are fused using a correlation-based strategy. This step ensures the optimal integration of texture features by weighting the contributions of each pathway according to their correlation with the original image, preserving critical diagnostic information. Finally, the Inverse Wavelet Transform is applied to the fused coefficients to reconstruct the denoised OCT image in the spatial domain. This reconstruction step maintains structural integrity and enhances diagnostic clarity by preserving essential spatial features. (3) Results: The MSE, PSNR, and SSIM indices of the proposed algorithm in this paper were 4.9052, 44.8603, and 0.9514, respectively, achieving commendable results compared to other algorithms. The Sobel, Prewitt, and Canny operators were utilized for edge detection on images, which validated the enhancement effect of the proposed algorithm on image edges. (4) Conclusions: The proposed algorithm in this paper exhibits an exceptional performance in noise suppression and detail preservation, demonstrating its potential application in OCT image denoising. Future research can further explore the adaptability and optimization directions of this algorithm in complex noise environments, aiming to provide more theoretical support and practical evidence for enhancing OCT image quality. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-a2cdba41fe2d4a08b2aa3b722b6f8c3e2025-08-20T03:32:28ZengMDPI AGApplied Sciences2076-34172025-06-011512655710.3390/app15126557Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNNFangfang Li0Qizhou Wu1Bei Jia2Zhicheng Yang3School of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, China(1) Background: Due to its imaging principle, OCT generates images laden with significant speckle noise. The quality of OCT images is a crucial factor influencing diagnostic effectiveness, highlighting the importance of OCT image denoising. (2) Methods: The OCT image undergoes a Discrete Wavelet Transform (DWT) to decompose it into multiple scales, isolating high-frequency wavelet coefficients that encapsulate fine texture details. These high-frequency coefficients are further processed using a Shift-Invariant Wavelet Transform (SWT) to generate an additional set of coefficients, ensuring an enhanced feature preservation and reduced artifacts. Both the original DWT high-frequency coefficients and their SWT-transformed counterparts are independently denoised using a Deep Neural Convolutional Network (DnCNN). This dual-pathway approach leverages the complementary strengths of both transform domains to suppress noise effectively. The denoised outputs from the two pathways are fused using a correlation-based strategy. This step ensures the optimal integration of texture features by weighting the contributions of each pathway according to their correlation with the original image, preserving critical diagnostic information. Finally, the Inverse Wavelet Transform is applied to the fused coefficients to reconstruct the denoised OCT image in the spatial domain. This reconstruction step maintains structural integrity and enhances diagnostic clarity by preserving essential spatial features. (3) Results: The MSE, PSNR, and SSIM indices of the proposed algorithm in this paper were 4.9052, 44.8603, and 0.9514, respectively, achieving commendable results compared to other algorithms. The Sobel, Prewitt, and Canny operators were utilized for edge detection on images, which validated the enhancement effect of the proposed algorithm on image edges. (4) Conclusions: The proposed algorithm in this paper exhibits an exceptional performance in noise suppression and detail preservation, demonstrating its potential application in OCT image denoising. Future research can further explore the adaptability and optimization directions of this algorithm in complex noise environments, aiming to provide more theoretical support and practical evidence for enhancing OCT image quality.https://www.mdpi.com/2076-3417/15/12/6557OCT imageimage denoisingdiscrete wavelet transformstationary wavelet transformdenoising convolutional neural network |
| spellingShingle | Fangfang Li Qizhou Wu Bei Jia Zhicheng Yang Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN Applied Sciences OCT image image denoising discrete wavelet transform stationary wavelet transform denoising convolutional neural network |
| title | Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN |
| title_full | Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN |
| title_fullStr | Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN |
| title_full_unstemmed | Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN |
| title_short | Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN |
| title_sort | speckle noise removal in oct images via wavelet transform and dncnn |
| topic | OCT image image denoising discrete wavelet transform stationary wavelet transform denoising convolutional neural network |
| url | https://www.mdpi.com/2076-3417/15/12/6557 |
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