Impulsive Noise

Impulsive noise, characterized by sporadic bursts of high-amplitude interference, poses a significant challenge to signal processing across various applications, from digital audio broadcasting to image processing. Current research focuses on developing robust algorithms and models, such as those based on sparse signal recovery, Markov-Middleton models, and M-estimators, to mitigate the effects of this noise, often incorporating machine learning techniques for data-driven solutions. These advancements are crucial for improving the reliability and performance of numerous systems, particularly in environments with unpredictable and harsh noise conditions, leading to enhanced signal quality and system robustness.