Speckle Pattern Denoising Using Wavelet Default Threshold Method

This paper presents a comprehensive approach for speckle pattern denoising using wavelet default threshold methodology. The implementation involves applying wavelet transform decomposition followed by thresholding operations using the default threshold values calculated based on the noise characteristics. For phase analysis, we demonstrate two complementary techniques: phase-shifting method for initial phase calculation and least-squares approach for optimal phase solution fitting, both incorporating phase unwrapping algorithms to resolve 2π ambiguities. These methodologies are implemented through systematic procedures where wavelet coefficients are processed using soft or hard thresholding functions, while phase unwrapping utilizes quality-guided or minimum-discontinuity algorithms. The presented techniques demonstrate broad applicability beyond speckle imaging, extending to various domains including digital image processing, signal processing, and optical measurement systems. This work provides valuable insights into wavelet-based denoising implementations and phase retrieval algorithms, encouraging further exploration of wavelet transform applications and advanced phase-solving methodologies.