Remote sensing image destriping by an ℓ0-based nonconvex model with overlapping group sparse hyper-Laplacian prior

In this paper, we propose an ℓ ₀ -based nonconvex optimization model with overlapping group sparse hyper-Laplacian prior (ℓ ₀ -OGSHL) to remove stripes from remote sensing images (RSIs) effectively. Specifically, we utilize the hyper-Laplacian prior with overlapping group sparsity (OGSHL) to characterize the properties of the underlying image. Additionally, the related ℓ ₀ -quasi equivalent is transformed into an easily solvable form by employing a mathematical program with equilibrium constraints (MPEC). Furthermore, the alternating direction method of multipliers (ADMM) algorithm is employed for resolving the equivalent nonconvex optimization model, and the complex OGSHL subproblem is addressed through the majorization-minimization (MM) method. Finally, the experimental results on the simulated datasets conclusively demonstrate the superior performance of the proposed method over the compared methods (with 1 3dB higher MPSNR), both quantitatively and visually. The code will be available after possible acceptance.