Per-Pixel Noise Estimation in Hyperspectral Images

Modeling of the underlying noise in a hyperspectral image reveals important information about the characteristics of the hyperspectral sensor and the image itself. While the focus in the literature has mostly been on the estimation of noise statistics, it is also of interest to estimate the actual noise present in each pixel, which can not only directly contribute to denoising of the image but can also aid other image processing algorithms exploiting such information. In this letter, we propose a novel method for per-pixel noise estimation that is also able to deal with spectral correlation in the noise. The method makes no assumptions on the behavior of the underlying noise. Simulation results show that the proposed method performs significantly better than the existing methods in cases where there is a correlation in the noise.