De-Interlacing Algorithm Using Weighted Least Squares

Abstract

This paper presents a weighted least squares-based intrafield de-interlacing algorithm. First, we formulate the estimation of the missing pixels as a maximum a posteriori (MAP) framework. We deduce the weighted least squares structure from MAP based on the analysis of the statistic model of the original high-resolution images and the associated statistical model of the given low-resolution images and original high-resolution images. The weights affect the estimation of the statistical model. We also design adaptive weights to match regions with different properties. The method is compared with other de-interlacing algorithms in terms of PSNR and SSIM objective quality measures and de-interlacing speed. It was found to provide excellent performance and the best quality-speed tradeoff among the methods studied.