Poisson Noise

Poisson noise, characterized by a variance equal to its mean, is a prevalent challenge in various fields, particularly in low-light imaging and count data analysis. Current research focuses on developing robust methods for denoising and analyzing data corrupted by Poisson noise, including advanced optimization techniques like the alternating direction method of multipliers (ADMM) and the use of Markov random fields and diffusion models to capture complex dependencies within the data. These advancements are crucial for improving the accuracy and reliability of analyses in diverse applications such as medical imaging, causal inference from count data, and recommender systems, where accurate handling of Poisson noise is essential for reliable results.