Multiplicative Noise

Multiplicative noise, a type of signal distortion where noise magnitude is proportional to the signal's amplitude, poses significant challenges in various fields, from image processing to system identification. Current research focuses on developing robust methods for removing or mitigating this noise, employing techniques ranging from stochastic differential equation-based diffusion models and deep learning architectures like convolutional neural networks and reservoir computing to advanced optimization algorithms such as CMA-ES. These efforts aim to improve the accuracy and efficiency of signal processing, data analysis, and control systems in applications where multiplicative noise is prevalent, such as medical imaging, radar technology, and dynamical systems modeling. The development of effective noise reduction techniques is crucial for enhancing the reliability and performance of numerous scientific instruments and technological systems.