## 1 Introduction  
Interferometry is a highly precise optical technique widely used for imaging or analyzing shape, deformation, or refractive index distribution [1]. A common phenomenon in interferometric systems is phase wrapping, which occurs when the measured phase exceeds the principal range of \(-\pi\) to \(\pi\), resulting in wrapped-phase images or maps. These wrapped-phase images contain essential information about the object's surface characteristics, but a phase unwrapping process is required to reconstruct the true phase distribution [2]. However, noise, particularly speckle and Gaussian noise, can complicate the unwrapping process and reduce overall accuracy [3, 4]. Speckle noise arises from the rough surface of the object under imaging and is highly non-stationary. It causes localized variations in intensity and phase, making it difficult to remove with conventional filtering and often leading to unwrapping errors [5; 6]. On the other hand, Gaussian noise introduces random fluctuations across both smooth and detailed regions, reducing the signal-to-noise ratio (SNR) [7; 8]. The combined presence of these noise types significantly impacts the quality of wrapped-phase maps, especially when simple algorithms are used for unwrapping. Therefore, an effective denoising process before phase unwrapping is crucial, as it can remove noise artifacts while preserving structural details, ensuring more reliable phase reconstruction. Traditional denoising approaches such as sine/cosine filters [3; 4] and windowed Fourier transforms [9; 10] have been used in wrapped-phase denoising, but these methods often involve high computational costs and require extensive parameter tuning, limiting their adaptability in practical applications. With the emergence of deep learning, denoising techniques have advanced significantly. Convolutional Neural Networks (CNNs) [11] and their variants have been successfully applied in optical domains such as synthetic aperture radar (SAR) imaging [12], multiscale decomposition [13], hyperspectral imaging [14], and optical coherence tomography (OCT) [15; 16]. These approaches have shown promising results in wrapped-phase denoising tasks as well [17]. For instance, the Denoising Convolutional Neural Network (DnCNN) [18] was introduced to remove Gaussian noise through supervised learning with paired training data. However, it is less effective against non-Gaussian noise such as speckle noise. To address the problems of speckle noise, various CNN-based networks have been developed. The fringe pattern denoising CNN (FPD-CNN) [19] was proposed for digital speckle pattern interferometry, while V-Net [20] facilitated fringe denoising and normalization. [PERSON] et al. [21] trained a CNN on real speckle-corrupted holograms, significantly improving denoising performance in digital holographic vibrometry. [PERSON] et al. [22] employed a conditional generative adversarial network (cGAN) to achieve high-quality speckle reduction, enhancing phase measurement accuracy. [PERSON] et al. [23] introduced a method for denoising wrapped-phase images, aimed at improving defect detection in structural diagnostics. Several hybrid models have also been explored. A convolutional and Fourier neural network architecture combining CNNs with Fourier neural operators was proposed to extract spatial and frequency features simultaneously [24]. [PERSON] et al. [25] proposed a speckle denoising convolutional neural network (SDCNN) aimed at suppressing speckle noise in digital speckle shearography, preserving texture through optimized loss functions. [PERSON] et al. [26] introduced a dilated-blocks-based deep convolution neural network (DBDNet) designed for high-speckle environments. Its extended version [27] employed a combined MS-SSIM and L1 loss to enhance denoising accuracy. [PERSON] et al. [28] presented an attention-driven denoising approach to address fringe patterns with variable density. [PERSON] et al.