Adaptive Least Squares Interpolation of Infrared Images

This paper presents an adaptive block-by-block least squares (LS) algorithm for the interpolation of infrared (IR) images. The suggested algorithm is based on the segmentation of the low resolution (LR) image into overlapping blocks and the interpolation of each block, separately. The purpose of the overlapping is to avoid edge effects between blocks. An iterative implementation of the proposed algorithm, which considers the image acquisition model, is used for the minimization of the estimation error in each block. A weight matrix of moderate dimensions is estimated in a small number of iterations to interpolate each block. This proposed algorithm avoids the large computational complexity resulting from the matrices of large dimensions required to interpolate the image as a whole. The performance of the proposed algorithm is compared with the standard as well as the warped distance optimal interpolation of maximal order with minimal support (O-MOMS) algorithm from the peak signal-to-noise ratio (PSNR) point of view. Numerical results reveal the superiority of the proposed LS algorithm to the cubic O-MOMS algorithm.