# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 3 Results
### OCT Image Denoising  
The denoising algorithms selected for OCT images [25] include mean filtering (with a 3 \(\times\) 3 filter window), median filtering (with a 3 \(\times\) 3 filter window), isotropic filtering, anisotropic filtering, non-local mean filtering (NLM), the DnCNN, and the algorithm proposed in this paper. Table 1 presents the denoising evaluation metrics for these algorithms. These quantitative results were derived from 304 optical coherence tomography (OCT) images acquired through a single 3 mm \(\times\) 3 mm scanning session, with each image processed independently using distinct denoising algorithms, and the reported values represent the arithmetic mean of performance metrics across the dataset. The experimental results reveal that the MSE, PSNR, and SSIM achieved by the proposed algorithm in this paper are 4.9052, 44.8603, and 0.9514, respectively, yielding excellent results compared to other algorithms. This study systematically validates the proposed denoising algorithm's balanced performance between structural fidelity preservation and noise suppression through the quantitative analysis of objective evaluation metrics. Experimental results demonstrate that the proposed method achieves an average SSIM improvement of 4.5% compared to the DnCNN, indicating a superior capability in preserving structural information such as image edges and textures. Concurrently, a 69.1% reduction in the MSE directly reflects the effective mitigation of pixel-wise reconstruction errors, while a 2.73 dB enhancement in the PSNR corroborates the algorithm's improvement in visual quality from a signal fidelity perspective. Notably, in high-detail preservation scenarios such as OCT image datasets, the proposed method maintains SSIM values above 0.95 while achieving a PSNR breakthrough of 44 dB. This performance validates the effectiveness of its multiscale feature fusion mechanism. Unlike conventional methods that struggle with the trade-off between denoising intensity and detail preservation, our approach achieves synergistic improvements across three critical dimensions, establishing a novel paradigm for the collaborative optimization of structural perception and noise suppression in image restoration tasks. A comprehensive analysis of the denoised image presented in Figure 6 yields the subsequent findings. Traditional filtering techniques, such as mean and median filtering, demonstrate a suboptimal efficacy in denoising applications, often leaving significant noise residuals within the processed images. Specifically, mean filtering exhibits pronounced deficiencies in this regard. Both isotropic and anisotropic filtering methods struggle to adequately eliminate noise, with the latter additionally introducing blurred edge textures. While the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MSE** & **PSNR (db)** & **SSIM** \\ \hline Mean & 33.8135 \(\pm\) 0.48 & 40.4853 \(\pm\) 1.32 & 0.5146 \(\pm\) 0.095 \\ Median & 27.8253 \(\pm\) 0.54 & 40.9086 \(\pm\) 0.98 & 0.4918 \(\pm\) 0.089 \\ Isotropic & 25.6421 \(\pm\) 0.38 & 41.0860 \(\pm\) 1.41 & 0.5433 \(\pm\) 0.054 \\ Anisotropic & 25.3058 \(\pm\) 0.41 & 41.1147 \(\pm\) 0.98 & 0.8308 \(\pm\) 0.045 \\ NLM & 24.0658 \(\pm\) 0.65 & 41.2238 \(\pm\) 1.84 & 0.8107 \(\pm\) 0.153 \\ DnCNN & 15.8561 \(\pm\) 0.22 & 42.1298 \(\pm\) 1.35 & 0.9102 \(\pm\) 0.112 \\ Our Proposed & 4.9052 \(\pm\) 0.32 & 44.8603 \(\pm\) 1.12 & 0.9514 \(\pm\) 0.068 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation metrics for image denoising performance. NLM and DnCNN algorithms perform commendably in noise reduction, they exhibit less clarity in preserving edge details compared to the proposed algorithm. In contrast, the novel approach presented herein excels in real-world image denoising, not only achieving superior noise elimination but also providing a more precise restoration of structural details and textures.