Joint Image Registration and Super-Resolution using Nonlinear Least Squares Method
Yu He, Kim–Hui Yap, Li Chen, Lap‐Pui Chau · 2007
This paper proposes a new algorithm to integrate image registration into image super-resolution (SR) by fusing multiple blurred low-resolution (LR) images to render a high-resolution (HR) image. Conventional super-resolution (SR) image reconstruction algorithms assume either the estimated motion (displacement) errors by existing registration methods are negligible or the displacement is known a priori. This assumption, however, is impractical as the performance of existing registration algorithms is still less than perfect. In view of this, we present a new estimation framework that performs joint image registration and HR reconstruction. An iterative scheme based on nonlinear least squares method is developed to estimate the motion shift (displacement) and HR image progressively. The motion model that is considered in this work includes both translation as well as rotation. Experimental results show that the proposed method is effective in performing image super-resolution.