  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.
