Multi-Image Restoration Method Combined with Total Generalized Variation and lp-Norm Regularizations

Image restoration is an important part of various applications, such as computer vision, robotics and remote sensing. However, recovering the underlying structures of the latent image contained in multi-image is a challenging problem because of the need to develop robust and fast algorithms. In this paper, a novel problem formulation for multi-image restoration problem is proposed. This novel formulation is composed of multi-data fidelity terms and a composite regularizer. The proposed regularizer consists of total generalized variation (TGV) and lp-norm. This multi-regularization method can simultaneously exploit the consistence of image pixels and promote the sparsity of natural signals. To deal with the resulting problem, we derive and implement the solution using alternating direction method of multipliers (ADMM). The effectiveness of our method is illustrated through extensive experiments on multi-image denoising and inpainting. Numerical results show that the proposed method is more efficient than competing algorithms, achieving better restoration performance.