Robust destriping of MODIS and hyperspectral data using a hybrid unidirectional total variation model

Abstract

Imaging from a degenerated push broom scanner usually leads to an undesired stripe noise which seriously affected the image quality. To eliminate this kind of artifact, a robust hybrid unidirectional total variation model is presented. The traditional unidirectional total variation model produces an excellent performance only on weak and moderate-amplitude stripe images while does a poor job on heavy ones. By introducing a simple weighted matrix, a hybrid unidirectional total variation model with two combined ℓ1 data-fidelity terms is launched to handle various stripe noises with different intensity. An efficient numerical algorithm based on the split Bregman iteration is developed to solve the hybrid ℓ1-regularized optimization problem. Comparative results on simulated and real striped images taken with MODIS and hyperspectral imaging systems demonstrated that the proposed method not only can effectively remove the stripe noise but also preserve the edge and detail information.