# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 4 Discussion  
One of the critical parameters in ocular fundus OCT images is the integrity of the structural layers of the ocular fundus cells, which manifests as clear edges in the images. Therefore, edge detection [26] in OCT images before and after denoising is also an important indicator for assessing the effectiveness of denoising. Sobel, Prewitt, and Canny are commonly used edge detection algorithms [27]. The Sobel operator detects image edges by calculating gradients in both horizontal and vertical directions. The Prewitt operator  
Figure 6: Comparison of denoising effects on OCT images. (**a**) Original OCT image; (**b**) mean; (**c**) median; (**d**) isotropic; (**e**) anisotropic; (**f**) non-local means; (**g**) DnCNN; and (**h**) our proposed algorithm.  
is very similar to the Sobel operator, as both perform edge detection by computing image gradients. However, the Sobel operator applies a weighting to the smoothness of the image, whereas the convolution kernel weights of the Prewitt operator are relatively uniform, without assigning a greater weight to the central part. The Canny operator is a multi-stage edge detection algorithm often regarded as one of the optimal edge detection methods. It can accurately detect image edges and extract complex and detailed edges, albeit with challenges in parameter selection.  
To verify the impact of the proposed algorithm on image edges, Sobel, Prewitt, and Canny operators were utilized for edge detection on images, and comparisons were made between edge detections before and after denoising to validate the effectiveness of the proposed algorithm, as shown in Figure 7. Specifically, Figure 7a-c displays the results of the edge detection using Sobel, Prewitt, and Canny operators on the original images, respectively, while Figure 7d-f presents the results of the edge detection using the same operators on the denoised images.  
Figure 7: Edge detection on OCT images before and after denoising using Sobel, Prewitt, and Canny: (**a**) edge detection map of original OCT image using Sobel; (**b**) edge detection map of original OCT image using Prewitt; (**c**) edge detection map of original OCT image using Canny; (**d**) edge detection map of denoised OCT image using Sobel; (**e**) edge detection map of denoised OCT image using Prewitt; and (**f**) edge detection map of denoised OCT image using Canny.  
As illustrated in Figure 7, the speckle noise present in OCT images adversely affects the identification of image edges, particularly the boundaries between retinal layers, which are significantly impacted by this noise. Following denoising, the image boundaries become distinct, and the edges appear smooth, thereby providing a solid foundation for subsequent image processing tasks.  
The texture of the OCT images can be disrupted by pathological conditions. The edge detection was performed on lesion sites affected by common diseases such as AMD, CNV, and DR, which exhibit distinct morphological distortions due to their different pathogeneses. The Canny edge detection algorithm was employed for this purpose, with results presented in Figure 8.  
As demonstrated in Figure 8, the proposed algorithm exhibits exceptional edge enhancement capabilities post-denoising, effectively extracting retinal layer boundaries across all tested conditions. For disease-specific retinal deformations and pathological regions,  
Figure 8: Denoised OCT images and Canny edge detection results for common retinal diseases: (**a**) AMD image denoising; (**b**) CNV image denoising; (**c**) DR image denoising; (**d**) AMD image edge detection; (**e**) CNV image edge detection; and (**f**) DR image edge detection.  
the algorithm demonstrates a robust edge detection performance, providing favorable conditions for subsequent segmentation tasks.