MT-OMP for hyperspectral imagery denoising with model parameter estimation
It is extensively accepted that much noise is included in hyperspectral imagery (HSI). Noise removal for HSI is an important but challenging task. Most denoising methods have one or more model parameters. For many algorithms, the denoising performance strongly depends on the values of parameters. In many cases, empirically selected parameters are not adaptive to various noise levels. Another challenge is the computational complexity. Since HSI has numerous bands, band by band HSI denoising is relatively time-consuming when compared to RGB or gray image. So a fast algorithm is preferred in practice. In this work, a multi-task orthogonal matching pursuit (MT-OMP) algorithm is proposed for l2,0 non-local sparse denoising. This greedy scheme is a multi-task extension of the famous OMP algorithm. The only parameter of MT-OMP is the sparse reconstruction error, which can be derived via noise variance. Furthermore, it is time-efficient and easy to implement. The experimental results show advantages of the proposed MT-OMP algorithm.