## 4 Results
### Experiments with Synthetic and Real Data
In contrast, WPD-Net and other networks achieve more effective noise reduction, ensuring improved phase consistency and reduced intensity distortion, as reflected in the corresponding difference maps. Figure 7: Comparison of denoising performance on wrapped-phase images with severe speckle noise (Variance = 0.1). (**a-1**) shows the input noisy wrapped-phase image, and (**a-2**) shows the corresponding clean ground truth. The denoised results and their corresponding difference maps are shown for each method: WPD-Net in (**b-1,b-2**), AD-CNN in (**c-1,c-2**), LGCT-Net in (**d-1,d-2**), DBDNet in (**e-1,e-2**), DBDNet2 in (**f-1,f-2**), and LRDUNet in (**g-1,g-2**). Moreover, we evaluate the performance of the proposed method under a challenging mixed-noise scenario, consisting of high speckle noise (variance = 0.1) and Gaussian noise (SNR = 0 dB). As shown in Figure 9(a-1), the input wrapped-phase image is severely degraded, while the corresponding noise-free image is presented in Figure 9(a-2). The denoised results obtained from different methods are shown: WPD-Net (Figure 9(b-1)), AD-CNN (Figure 9(c-1)), LGCT-Net (Figure 9(d-1)), DBDNet (Figure 9(e-1)), DBDNet2 (Figure 9(f-1)), and LRDUNet (g-1), with their corresponding difference maps in Figure 9(b-2-g-2). It is important to emphasize that all compared networks were retrained using the same redesigned mixed-noise dataset as WPD-Net (see Section 3.2), ensuring a fair and consistent evaluation. Despite this, most methods struggle to fully remove noise, especially in areas with sharp tran  
Figure 8: Comparison of denoising performance on wrapped-phase images with severe Gaussian noise (SNR = 0 dB). (**a-1**) shows the input noisy wrapped-phase image, while (**a-2**) shows the corresponding clean ground truth. The denoised outputs and their difference maps are provided for WPD-Net (**b-1,b-2**), AD-CNN (**c-1,c-2**), LGCT-Net (**d-1,d-2**), DBDNet (**e-1,e-2**), DBDNet2 (**f-1,f-2**), and LRDUNet (**g-1,g-2**). sitions and boundaries, resulting in visible residual artifacts. In contrast, WPD-Net effectively reduced both speckle and Gaussian noise while preserving structural continuity and phase integrity. These visual results indicate that although careful dataset design contributes to improved performance, it alone is insufficient. Architectural enhancements are also necessary to achieve robust denoising under complex noise conditions. The superior performance of WPD-Net is attributed to its architectural innovations, particularly the integration of RDABs and the DFF module, which enables more effective multi-scale feature extraction and targeted noise suppression. Figure 9: Comparison of denoising performance on wrapped-phase images with combined speckle (Variance = 0.1) and Gaussian (SNR = 0 dB) noise. (**a-1**) shows the input noisy wrapped-phase image, while (**a-2**) presents the corresponding clean ground truth. The denoised results and their difference maps are shown for WPD-Net (**b-1,b-2**), AD-CNN (**c-1,c-2**), LGCT-Net (**d-1,d-2**), DBDNet (**e-1,e-2**), DBDNet2 (**f-1,f-2**), and LRDUNet (**g-1,g-2**). Table 3 presents the MSE, PSNR, and SSIM metrics for all networks shown in Figures 7-9 providing a quantitative evaluation under both high speckle and Gaussian noise conditions. The results clearly demonstrate that WPD-Net achieves superior noise reduction while effectively preserving phase structure. To further validate the effectiveness of our method, we evaluated WPD-Net on experimentally acquired wrapped-phase images using a full-field digital holographic interferometry setup. This setup enables the simultaneous capture of the entire surface of a sample in a single exposure, offering high-resolution phase information at a time without the need for mechanical scanning [39]. Two types of samples were tested: an acrylic plate and a PDMS (polydimethylsiloxane) specimen. The acrylic sample, characterized by a relatively smooth and reflective surface, exhibited clear and continuous wrapped-phase fringes with a moderate noise level.