Image Denoising Using Non-Subsampled Contourlet Transform with Multiscale Geometric Analysis

The Non-Subsampled Contourlet Transform (NSCT) serves as a crucial technique for image denoising and multiscale geometric analysis. This method employs a pyramidal decomposition structure combined with directional filter banks to achieve multiscale image decomposition and reconstruction, effectively eliminating noise while preserving image details. In implementation, the NSCT algorithm typically involves Laplacian pyramid decomposition followed by directional filtering at each scale, where the absence of downsampling prevents aliasing artifacts. The denoising process often incorporates thresholding techniques (e.g., soft/hard thresholding) applied to the transform coefficients across different scales and directions. With broad applications in image processing, NSCT can be utilized for image enhancement, feature extraction, and other computer vision tasks. Research and implementation of NSCT—often programmed using MATLAB/Python with libraries like PyWavelets or custom filter banks—continue to expand possibilities and drive innovation in digital image processing领域.