3D sparse coding based denoising of hyperspectral images

Hyperspectral images (HSIs) are often contaminated by noise, in order to remove the image noise efficiently and acquire excellent results. We propose a new denoising method based on 3D sparse coding. Firstly, to make full use of spectral information of hyperspectral data, we extract patches from HSIs and each patch contains the same area of different band. Secondly, we use aforementioned method to extract all patches and train these patches, the dictionary can be obtained, further calculate sparse coefficients. Finally, we can restore the HISs through the dictionary and the sparse coefficients. Experiments are implemented using the HSIs collected by AVIRIS and ROSIS. Results indicate that compared with common 2D sparse coding method, 3D sparse method can effectively improve the restoration performance for both subjective visual and objective evaluation criterion.