Deep Convolutional Framelet Denoising for Panoramic by Mixed Wavelet Integration

TL;DR

This paper introduces a novel denoising method for panoramic X-ray images by integrating mixed wavelet transforms with a U-Net neural network, improving noise reduction performance over traditional techniques.

Contribution

It proposes combining Daubechies wavelet with the waveform in a U-Net architecture for enhanced image denoising, which is a new approach in this context.

Findings

Increased PSNR and SSIM metrics demonstrate improved denoising performance.

The one-wave network's effectiveness increased from 0.5% to 1.2%.

The method outperforms traditional denoising techniques like BM3D and low-pass filters.

Abstract

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