本文提出了一种改进U-Net散斑抑制方法,该方法结合了Inception、残差结构和注意力模块,应用于具有不同噪声级别的包裹相位图像。将所提出的方法与传统的降噪方法以及现有的深度学习降噪方法进行了对比,仿真与实验结果表明,所提出的方法在不同噪声级别下具有更好的散斑抑制效果。此外,我们对降噪后的包裹相位进行了相位重建,对比了不同方法降噪后的相位精度,结果表明,该方法在实际应用中能够有效抑制散斑噪声,取得了较好的效果。This paper proposes an improved U-Net speckle suppression method that integrates Inception and residual structures with attention modules, applied to wrapped phase images with different noise levels. The proposed method is compared with traditional denoising methods as well as existing deep learning-based denoising techniques. Experimental results show that our method achieves better speckle suppression across various noise levels. Furthermore, we performed phase reconstruction on the denoised wrapped phase images and compared the phase accuracy of different denoising methods. The results show that the proposed method can effectively suppress speckle noise in practical applications and achieve satisfactory performance.
