Poisson Inverse Problem

The Poisson inverse problem focuses on recovering an underlying signal from noisy, Poisson-distributed measurements, a challenge arising in diverse fields like medical imaging and computational biology. Current research emphasizes developing efficient and accurate solution methods, exploring machine learning approaches such as neural networks (including graph neural networks and neural field transformers) and advanced optimization algorithms like stochastic dual averaging and Bregman proximal gradient methods. These advancements aim to overcome the computational limitations of traditional methods, improving the speed and accuracy of solving Poisson inverse problems across various applications, ultimately leading to better data analysis and interpretation.