Enhancing quality and removing noise during preprocessing is one of the most critical steps in image processing. X-ray images are created by photons colliding with atoms and the variation in scattered noise absorption. This noise leads to a deterioration in the graph's medical quality and, at times, results in repetition, thereby increasing the patient's effective dose. One of the most critical challenges in this area has consistently been lowering the image noise. Techniques like BM3d, low-pass filters, and Autoencoder have taken this step. Owing to their structural design and high rate of repetition, neural networks employing diverse architectures have, over the past decade, achieved noise reduction with satisfactory outcomes, surpassing the traditional BM3D and low-pass filters. The combination of the Hankel matrix with neural networks represents one of these configurations. The Hankel matrix aims to identify a local circle by separating individual values into local and non-local components, utilizing a non-local matrix. A non-local matrix can be created using the wave or DCT. This paper suggests integrating the waveform with the Daubechies (D4) wavelet due to its higher energy concentration and employs the u-Net neural network architecture, which incorporates the waveform exclusively at each stage. The outcomes were evaluated using the PSNR and SSIM criteria, and the outcomes were verified by using various waves. The effectiveness of a one-wave network has increased from 0.5% to 1.2%, according to studies done on other datasets

翻译：在图像处理中，提升质量与去除噪声是预处理中最关键的步骤之一。X射线图像由光子与原子碰撞以及散射噪声吸收的变化所产生。这种噪声会导致图像医学质量的下降，有时还会造成重复扫描，从而增加患者的有效辐射剂量。该领域最关键的挑战之一始终是降低图像噪声。诸如BM3D、低通滤波器和自编码器等技术已在此方面取得进展。得益于其结构设计和高重复率，采用多样化架构的神经网络在过去十年中实现了噪声降低，并取得了令人满意的结果，超越了传统的BM3D和低通滤波器。汉克尔矩阵与神经网络的结合代表了其中一种配置。汉克尔矩阵旨在通过将单个值分离为局部和非局部分量来识别局部循环，并利用非局部矩阵。非局部矩阵可使用波形或DCT创建。本文提出将波形与Daubechies（D4）小波集成，因其具有更高的能量集中度，并采用u-Net神经网络架构，该架构在每一阶段均专门融合了波形。结果使用PSNR和SSIM标准进行评估，并通过使用多种波形进行验证。根据在其他数据集上进行的研究，单波网络的有效性提高了0.5%至1.2%。