Speed-up generalized morphological component analysis technology used in remote sensing image inpainting application

Abstract

In this paper, we introduced a remote sensing image inpainting method based on speed-up generalized morphological component analysis (SGMCA). Due to its capability to represent and separate the morphological diversities, generalized morphological component analysis (GMCA) algorithm is a state-of-the-art image inpainting method. SGMCA algorithm introduced in this paper can accelerate the iterative process of GMCA algorithm. By adding some more assumptions to GMCA algorithm, SGMCA algorithm is proven as a much faster algorithm which can handle very large-scale problems. Several experiments illustrate that SGMCA algorithm can recover the remote sensing images with different patterns of missing pixels. It is even hard to distinguish the original remote sensing image from the recovered image through visual effect. The peak signal to noise ratio and structural similarity indices explain why the salient visual effect is obtained, and confirm the marvelous inpainting capability of SGMCA algorithm. Quantitative analysis on time consumption proves that SGMCA algorithm can greatly improve the iterative speed of GMCA algorithm, indeed.