## 4 Results
### Experiments with Synthetic and Real Data
The experimentally acquired noisy wrapped-phase image, having an SNR of 6.57 dB, is shown in Figure 10(a-1). The denoised results are presented in Figure 10(b-1-g-1), using WPD-Net, AD-CNN, LGCT-Net, DBDNet, DBDNet2, and LRDUNet, respectively. The WPD-Net output shows the highest quality restoration with improved clarity and reduced noise, while other methods leave visible residual noise or artifacts. We further imaged a PDMS sample, which presented a much more challenging case due to its highly non-uniform and elastic surface. This led to densely packed and irregular wrapped-phase patterns, making noise suppression particularly difficult. The noisy wrapped-phase image for PDMS with an SNR of 6.52 dB is shown in Figure 10(a-2). The denoising results using the various methods are displayed in Figure 10(b-2-g-2). While all methods attempted to restore the phase pattern, WPD-Net most effectively suppressed the complex noise and preserved the fine structural details of the wrap contours. Competing methods struggled to fully denoise the image and exhibited artifacts, particularly around high-gradient regions. To further challenge the denoising capabilities of WPD-Net, we imaged an additional PDMS sample that had water droplets on it and boundary leaks. This scenario introduced phase discontinuities, disconnected wrap lines, and sharp phase transitions, rather than only dense wrapping. The noisy wrapped-phase image used for this case, with an SNR of 4.79 dB, is shown in Figure 10(a-3). The corresponding denoised outputs using the same set of methods are illustrated in Figure 10(b-3-g-3). Despite the highly irregular nature of this sample, WPD-Net demonstrated robust performance, effectively recovering the structural layout of the phase wraps while minimizing noise. While LGCT-Net and LRDUNet showed some success, other models failed to handle the noise complexity and discontinuities. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Speckle Noise**} & \multicolumn{3}{c}{**Gaussian Noise**} & \multicolumn{3}{c}{**Combined Noise**} \\ \cline{2-10}  & **MSE** & **PSNR** & **SSIM** & **MSE** & **PSNR** & **SSIM** & **MSE** & **PSNR** & **SSIM** \\ \hline DBDNet [26] & 0.0351 & 18.674 & 0.884 & 0.0412 & 17.935 & 0.827 & 0.2163 & 8.536 & 0.621 \\ \hline DBDNet2 [27] & 0.0103 & 22.455 & 0.963 & 0.0185 & 22.476 & 0.958 & 0.0852 & 19.745 & 0.863 \\ \hline AD-CNN [28] & 0.0098 & 25.036 & 0.974 & 0.0106 & 24.962 & 0.971 & 0.0983 & 17.627 & 0.826 \\ \hline LGCT-Net [29] & 0.0146 & 21.945 & 0.953 & 0.0284 & 20.854 & 0.933 & 0.0617 & 20.529 & 0.857 \\ \hline LRDUNet [34] & 0.0082 & 24.015 & 0.975 & 0.0153 & 23.711 & 0.964 & 0.0994 & 18.262 & 0.878 \\ \hline WPD-Net & 0.0068 & 26.586 & 0.987 & 0.0085 & 25.981 & 0.973 & 0.0135 & 23.871 & 0.959 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison of simulated wrapped-phase patterns (Figures 7–9). Overall, these experimental results confirm that WPD-Net is not only effective on synthetic data but also reliable with real-world interferometric data. Its ability to generalize to challenging environments, including sharp transitions, disconnected phase regions, and irregular surface profiles, highlights its practical utility in wrapped-phase image denoising tasks. Figure 10: Experimental denoising results on wrapped-phase images acquired using full-field digital holographic interferometry. **(a-1)** Noisy wrapped-phase image of an acrylic sample, with SNR = 6.57 dB. **(a-2)** Noisy input of a PDMS sample with dense wraps and a complex surface profile, with SNR = 6.52 dB. **(a-3)** Noisy input of a challenging PDMS case with leaky boundaries and discontinuous wraps, with SNR = 4.79 dB. Denoised outputs are shown for **(b-1–b-3)** WPD-Net, **(c-1-c-3)** AD-CNN, **(d-1-d-3)** LGCT-Net, **(e-1–e-3)** DBDNet, **(f-1–f-3)** DBDNet2, and **(g-1–g-3)** LRDUNet.