## 1 Introduction
[29] proposed the local-global channel transformer network (LGCT-Net), which integrates transformers [30] with CNNs to improve the performance of discontinuous and diverse phase patterns. [PERSON] et al. [31] proposed a phase denoising network (PDNet) for InSAR phase denoising, which learns non-local self-similarity from diverse wrapped-phase images and filters noise without relying on kernel-based averaging, thereby preserving resolution and phase integrity. For batch denoising of images obtained through electronic speckle pattern interferometry (ESPI), [PERSON] et al. [32] and [PERSON] et al. [33] proposed different approaches. [PERSON]'s CNN-based framework processed multiple noisy images in parallel, while [PERSON]'s multi-dilated dense network (MDD-Net) focused on preserving structural integrity. Similarly, [PERSON] et al. [34] implemented a U-Net-based model and [PERSON] et al. [35] introduced a deep CNN (DCNN) for generalized fringe pattern denoising. Despite recent progress, several challenges still remain in wrapped-phase image denoising. Most existing methods are designed to handle either Gaussian or speckle noise individually but often fail when both types are present. This difficulty arises from the fundamental differences between the two: Gaussian noise is additive and statistically uniform, while speckle noise is multiplicative and highly variable. These differences require distinct strategies for effective removal, making it hard for a single model to perform well across both noise types. In addition, many current techniques demand significant computational resources and manual parameter tuning, making them less suitable for real-time applications. Their dependence on customized training datasets also limits adaptability and scalability. These limitations highlight the need for a more efficient and general approach that can handle diverse noise conditions with accuracy and consistency. To address the challenges in wrapped-phase denoising, we introduce WPD-Net (Wrapped-Phase Denoising Network), a robust and lightweight deep learning model designed to effectively handle both Gaussian and speckle noises. The network features a carefully structured architecture that begins with multiple convolutional layers for initial feature extraction, followed by residual dense attention blocks (RDABs). These RDABs extend conventional residual dense blocks by incorporating attention mechanisms, allowing the network to focus more effectively on relevant noisy regions and improve feature propagation. A dense feature fusion (DFF) module further combines hierarchical features to refine details and preserve the structural integrity of wrapped-phase images. Together, these components enable WPD-Net to capture complex noise patterns and restore clean high-quality phase images, while maintaining fast and accurate denoising performance without the need for large-scale training datasets. The main contributions of this study are as follows:  
* A synthetic dataset is generated by combining a ramp function and multiple Gaussian functions, incorporating various degrees of randomness and noise levels to enhance the model's capability to handle diverse noise conditions. * We propose WPD-Net, specifically designed to extract phase information distorted by noise while preserving fine structural details. The architecture includes RDABs that enhance phase reconstruction. * Instead of using only the mean squared error (MSE) loss, we employ a dynamic hybrid loss function that adaptively balances Structural Similarity Index (SSIM) and MSE during training for optimal intensity and structural preservation. To evaluate the performance of the proposed WPD-Net, we conduct experiments on both synthetic data and real data acquired from a custom-built full-field digital holographic interferometry setup. For quantitative assessment, we use three evaluation metrics: MSE, peak signal-to-noise ratio (PSNR), and SSIM. Comparisons are also made with other deep learning-based methods.