TriDeFusion: Enhanced denoising algorithm for 3D fluorescence microscopy images integrating modified Noise2Noise and Non-local means

Fluorescence microscopy is a technique for obtaining images of luminous objects of small size. It is widely used in fields ranging from materials science to neurobiology. Fluorescence microscopy has several advantages over other forms of microscopy, offering high sensitivity and specificity. However, it often results in images with noise and distortions, complicating subsequent analysis. This paper introduces the TriDeFusion algorithm for 3D image denoising, integrating Non-Local Means (NLM) and Modified Noise2Noise (N2N) techniques. Our results show that TriDeFusion significantly improves image quality, particularly in preserving details while reducing noise. In experiments with synthetic data, the combined methods outperformed individual approaches in both Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) metrics, achieving up to a 54% reduction in RMSE and a 20% increase in PSNR. For real data, our algorithm demonstrated a significant reduction of noise mean intensity by over 50% and variance by 33%, confirming its robustness and effectiveness across different noise levels and data types.