  Hyperspectral imaging with high spectral resolution plays an important role
in finding objects, identifying materials, or detecting processes. The decrease
of the widths of spectral bands leads to a decrease in the signal-to-noise
ratio (SNR) of measurements. The decreased SNR reduces the reliability of
measured features or information extracted from HSIs. Furthermore, the image
degradations linked with various mechanisms also result in different types of
noise, such as Gaussian noise, impulse noise, deadlines, and stripes. This
paper introduces a fast and parameter-free hyperspectral image mixed noise
removal method (termed FastHyMix), which characterizes the complex distribution
of mixed noise by using a Gaussian mixture model and exploits two main
characteristics of hyperspectral data, namely low-rankness in the spectral
domain and high correlation in the spatial domain. The Gaussian mixture model
enables us to make a good estimation of Gaussian noise intensity and the
location of sparse noise. The proposed method takes advantage of the
low-rankness using subspace representation and the spatial correlation of HSIs
by adding a powerful deep image prior, which is extracted from a neural
denoising network. An exhaustive array of experiments and comparisons with
state-of-the-art denoisers were carried out. The experimental results show
significant improvement in both synthetic and real datasets. A MATLAB demo of
this work will be available at https://github.com/LinaZhuang for the sake of
reproducibility.
