Image Denoising Using VisuShrink Wavelet Threshold Method

This package provides comprehensive image denoising capabilities utilizing the VisuShrink wavelet threshold method. Image processing represents a crucial application in digital signal processing, as it enhances image clarity and facilitates easier processing and analysis. The implementation employs discrete wavelet transform (DWT) decomposition to separate image components across different frequency bands. Image denoising serves as a fundamental step in image processing workflows, significantly improving image quality by reducing various types of noise artifacts. The VisuShrink wavelet threshold approach, implemented through optimized algorithms in this package, performs noise reduction by applying universal thresholding to wavelet coefficients. The core algorithm calculates the threshold value using σ√(2logN), where σ represents the noise standard deviation and N denotes the number of wavelet coefficients. Key functions in the package include wavelet decomposition using popular families (Daubechies, Symlets), threshold calculation modules, and wavelet reconstruction routines. The thresholding operation is implemented through both hard and soft thresholding methods, allowing users to select the most appropriate approach for their specific image characteristics. By leveraging this package, users can efficiently perform advanced image processing tasks and achieve superior quality image results with reduced computational complexity. The implementation includes automatic noise estimation routines and supports various image formats through integrated preprocessing modules.