X-CT Image Processing Using Wavelet Decomposition for Noise Removal

This document presents a MATLAB-based approach for X-CT image processing utilizing wavelet decomposition techniques for noise reduction. The implementation typically involves using MATLAB's wavelet toolbox functions such as wavedec2 for 2D wavelet decomposition and wdencmp for denoising. The core algorithm follows a standard workflow: first performing multi-level wavelet decomposition on the CT image, then applying thresholding techniques (soft or hard thresholding) to the detail coefficients, and finally reconstructing the denoised image using waverec2. Based on this foundation, we can further discuss the advantages and limitations of these methods, along with strategies for parameter adjustment and optimization according to specific application scenarios. For instance, one could investigate how different wavelet basis functions (e.g., Haar, Daubechies, Symlets) impact noise removal effectiveness, or explore how to leverage additional MATLAB tools and functions like imfilter for spatial filtering or medfilt2 for median filtering to optimize the image processing pipeline. Furthermore, the discussion may include relevant research findings and practical experiences to help readers gain deeper understanding and improve implementation of these techniques, such as comparing different threshold selection methods (VisuShrink, SureShrink) or evaluating performance metrics using functions like psnr and ssim.