Image Denoising Implementation Using MATLAB Programming

This program is designed for image denoising implementation using MATLAB, leveraging the dual-tree complex wavelet transform technique. Image denoising is a process of eliminating noise from digital images, and by employing DTCWT, we can effectively reduce image noise while enhancing overall image quality. The implementation involves several key steps programmed in MATLAB: First, we load the target image and convert it to grayscale format using MATLAB's image processing functions like `imread()` and `rgb2gray()`. Next, we apply the dual-tree complex wavelet transform to decompose the image into multiple frequency subbands - this transform provides better directional selectivity and shift-invariance compared to standard wavelet transforms. The core algorithm then performs thresholding operations on each subband coefficient, where coefficients below a specified threshold (determined by noise estimation algorithms) are set to zero to eliminate noise components. Finally, we reconstruct the denoised image through inverse DTCWT transformation. The program includes customizable parameters for threshold selection methods (soft/hard thresholding) and decomposition levels. Through this implementation, users can efficiently denoise images and obtain clearer, higher-quality results with well-documented MATLAB code structure.