Real-time wavelet threshold denoising for laser speckle blood flow imaging

Abstract

We present a real-time denoising method for laser speckle contrast imaging (LSCI) that combines a logarithmic homomorphic transform with wavelet decomposition and adaptive thresholding. Experimental results on flow phantoms demonstrate improved linearity between the blood-flow index (BFI) and true velocity, while in vivo data show superior performance in enhancing image quality (higher Peak Signal-to-Noise Ratio/Mean Structural Similarity Index) and stabilizing BFI estimates, as well as reduced noise fluctuations compared to temporal-contrast, non-local means, Block-Matching and 3D Filtering, and variational mode decomposition. The method effectively suppresses high-frequency noise while preserving microvascular details, achieving higher PSNR/MSSIM and lower Root Mean Square Error. It processes a 4 K frame in 50 milliseconds with GPU acceleration, meeting real-time requirements. With fixed hyperparameters and adaptive thresholding, the method avoids the need for scene-specific tuning, offering robustness across varying noise levels and imaging conditions, making it a promising candidate for clinical LSCI applications.