Fast Algorithm for Nonsubsampled Contourlet Transform

Abstract

The multiscale geometric analysis (MGA) has been recognized as an effective strategy for image processing. As one of the discrete tools of MGA, the nonsubsampled contourlet transform (NSCT) has been widely used for image denoising, image fusion, image enhancement, feature extraction and so on. However, the processing performance is limited due to its high redundancy, and leading to an intensive computational efficiency. Therefore, its fast algorithm is desired in practice. In this paper, we adopt an optimized directional filter bank (DFB) and embed it into the NSCT to significantly accelerate the computational speed while keeping slight loss of the reconstructed performance. Experimental results show that the reconstructed image quality can satisfy the human visual system. Moreover, the improved NSCT has a speed about several times than that of the traditional one. Experimental results on image denoising also validate the feasibility and efficiency of the proposed method.