Image Denoising Using Contourlet Transform, Wavelet Transform, and Related Methods

In this implementation, we utilize contourlet transforms, wavelet transforms, and related methods for image denoising applications. The contourlet transform effectively captures directional information and contours in images through a double filter bank structure, while wavelet transforms provide multi-resolution analysis using decomposition and reconstruction filters. After thorough debugging and testing cycles involving parameter optimization and threshold selection algorithms, we confirm that these methods are fully operational and deliver excellent denoising performance. Through proper implementation of thresholding functions (such as soft/hard thresholding) and inverse transformation procedures, we can effectively eliminate various types of image noise while preserving important image features. The application of these advanced transform-domain techniques represents significant progress in image processing, enabling more sophisticated analysis and manipulation of image data through computationally efficient algorithms.