Hyperspectral image destriping method based on nonlocal low-rank and total variation

Aiming at the problem of limited destriping performance caused by ignoring the non-local similarity of strips in existing methods, a hyperspectral image (HSI) destriping method based on non-local low rank tensor decomposition and total variation is proposed. This method considers the nonlocal similarity of the strips, and uses the tensor nonlocal low rank decomposition to approximate the nonlocal strips. Combining the direction and structural sparse features of the strips, they are effectively separated from the degraded image. Meanwhile, the local and nonlocal similarity of HSIs are considered together to reduce spectral distortion. Simulation results show that the proposed method accurately estimates and separates the strips, recovers the image information affected by the strips, and overcomes the problem of strip residue by considering the non-local similarity of strips and images.