## 4 Results
### Experiments with Synthetic and Real Data  
To assess the performance of WPD-Net, we evaluate it with two phase images randomly selected from the speckle noise dataset, each with different noise levels. Figure 5 presents two noisy wrapped-phase images: Figure 5(a-1) with moderate speckle noise (variance = 0.05) and Figure 5(a-2) with high speckle noise (variance = 0.08). Their corresponding clean noise-free ground truth images are shown in Figure 5(b-1) and Figure 5(b-2), respectively. The denoised outputs produced by WPD-Net are displayed in Figure 5(c-1,c-2), demonstrating that the network effectively suppresses speckle noise while preserving fine structural details, even under high noise conditions. Figure 5: Denoising performance on simulated wrapped-phase images with speckle noise. (**a-1,a-2**) Noisy input images with moderate (variance = 0.05) and high (variance = 0.08) speckle noise, respectively; (**b-1,b-2**) Corresponding ground truth images; (**c-1,c-2**) Denoised outputs from WPD-Net. Next, we evaluate the effectiveness of WPD-Net with two other phase images selected from the Gaussian noise dataset. Figure 6 shows the input noisy wrapped-phase images: Figure 6(a-1) with moderate (SNR = 10 dB) and Figure 6(a-2) severe Gaussian noises (SNR = 5 dB). Their corresponding ground truth images are shown in Figure 6(b-1,b-2), while the denoised results produced by WPD-Net are displayed in Figure 6(c-1,c-2). The visual comparison demonstrates that WPD-Net effectively removes Gaussian noise while preserving fine structural details and maintaining phase continuity. Table 2 presents the performance metrics for simulated speckle noise and Gaussian noise. We further compare WPD-Net with other deep learning-based methods for both speckle and Gaussian noise cases in Figure 7. For high-speckle noise (variance = 0.1), a wrapped-phase image is shown in Figure 7(a-1), alongside its corresponding clean noise-free image in Figure 7(a-2). The outputs produced by various methods are presented, including WPD-Net (Figure 7(b-1)), AD-CNN (Figure 7(c-1)), LGCT-Net (Figure 7(d-1)), DBDNet (Figure 7(e-1)), DBDNet2 (Figure 7(f-1)), and LRDUNet (Figure 7(g-1)). Their corresponding difference maps with respect to the ground truth are shown in Figure 7(b-2) through Figure 7(g-2), respectively. Due to the high noise level, DBDNet shows visible phase errors, especially around the areas that have large phase variations. In contrast, WPD-Net and other networks demonstrate better noise suppression and preserve structural consistency, as evidenced by the cleaner difference maps. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **MSE** & **PSNR** & **SSIM** \\ \hline Image with Speckle noise & 0.0056 & 27.68 & 0.989 \\ of Variance 0.05 & 0.0069 & 26.93 & 0.979 \\ Image with Gaussian noise & 0.0062 & 27.57 & 0.981 \\ of SNR 10 & 0.0073 & 25.72 & 0.975 \\ of SNR 5 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative metrics for simulated wrapped-phase patterns with different noises. Figure 6: Denoising performance on simulated wrapped-phase images with Gaussian noise. (**a-1,a-2**) Noisy input images with moderate (SNR = 10 dB) and severe (SNR = 5 dB) Gaussian noise, respectively; (**b-1,b-2**) Corresponding ground truth images; (**c-1,c-2**) Denoised outputs from WPD-Net. A similar comparison is performed for Gaussian noise with an SNR of 0 dB, as shown in Figure 8(a-1), with its corresponding clean noise-free image in Figure 8(a-2). The results denoised with other various methods are displayed: WPD-Net (Figure 8(b-1)), AD-CNN (Figure 8(c-1)), LGCT-Net (Figure 8(d-1)), DBDNet (Figure 8(e-1)), DBDNet2 (Figure 8(f-1)), and LRDUNet (g-1). Their respective difference maps are shown in Figure 8(b-2-g-2). Due to the significant intensity fluctuations caused by Gaussian noise, DBDNet and LGCT-Net show limitations in restoring phase continuity, resulting in visible artifacts.