Hyperspectral image denoising based on the similar spectra approaching

The local-area-based methods that utilize the weighted average of the neighbor pixels to replace the central noisy pixel, are widely used for hyperspectral image (HSI) denoising due to their good spectrum preservation performance. However, most existing local-area-based denoising methods are originally designed for the RGB image without considering the special characteristic of HSI, and they usually require a parameter to obtain the weights of the neighbor pixels. In this paper, based on the linear mixed model of HSI, we derive a significant property of the noise-free HSI that each pixel can be linearly represented by its neighbor pixels. Inspired by this property, we develop a simple but effective local-area-based denoising method, in which the weights are obtained without any parameters. Moreover, our method can automatically select those neighbor pixels which are similar to the central pixel, so the overall shape of the spectrum to be denoised can be effectively maintained. Experiments on synthetic data and real data demonstrate that the proposed denoising method is superior to the state-of-the-art methods.