MATLAB Source Code for Wavelet-Based Image Denoising

Using the content discussed, I present the following MATLAB source code for wavelet-based image denoising. I hope this proves helpful! Below are detailed instructions and usage guidelines for the program: First, import the image file into the MATLAB environment. This can be achieved using functions like `imread()` to load common image formats (e.g., JPEG, PNG) into a matrix representation. Next, apply wavelet transform functions to decompose the image. MATLAB’s Wavelet Toolbox provides functions such as `wavedec2()` for 2D discrete wavelet decomposition, which breaks down the image into approximation and detail coefficients across multiple resolution levels. Then, select an appropriate wavelet denoising method based on the noise type and intensity, such as soft-thresholding or hard-thresholding techniques. For instance, `wthresh()` can be used to apply thresholding to the wavelet coefficients, where soft-thresholding reduces noise more smoothly while preserving edges. Proceed to denoise each wavelet coefficient by applying the chosen thresholding method to the detail coefficients. This step typically involves setting a threshold value (e.g., using `thselect()` for adaptive threshold estimation) and modifying coefficients below this threshold to suppress noise. After denoising, use the inverse wavelet transform function to reconstruct the denoised image from the modified coefficients. The `waverec2()` function can reassemble the image from the denoised approximation and detail coefficients. Finally, adjust parameters like threshold values and wavelet types (e.g., Daubechies, Symlets) to optimize denoising performance. Iterative testing with metrics like PSNR (Peak Signal-to-Noise Ratio) can help fine-tune results. We hope these guidelines assist you in successfully implementing wavelet-based image denoising in MATLAB. If you have any questions or need further assistance, feel free to ask. Best of luck with your implementation!