Rotation invariant multi-frame image super resolution reconstruction using Pseudo Zernike Moments

Abstract

The purpose of multi-frame super resolution (SR) is to combine multiple low resolution (LR) images to produce one high resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To address this problem, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted average of all its neighboring pixels in all LR images. However, in case of rotation between LR images, comparing the gray level of blocks is not a suitable criterion for calculating the weight. Hence, magnitude of Zernike Moments (ZM) has been used as a rotation invariant feature. Considering the more robustness of Pseudo Zernike Moments (PZM) to noise and its higher description capability for the same order compared to ZM, in this paper, we propose a new method based on the magnitude of PZM as a rotation invariant descriptor for representing the pixels in the weight calculation. Also, due to the fact that the phase of PZM provides significant information for image reconstruction, we propose a new phase-based PZM descriptor for SR by making the phase coefficients invariant to rotation. Experimental results on several image sequences demonstrate that the proposed algorithm outperforms other currently popular SR techniques from the viewpoint of PSNR, SSIM and visual image quality.