Adaptively regularized muti-frame image super-resolution reconstruction
自适应正则化多幅影像超分辨率重建

Image super-resolution reconstruction has been a hot research topic in recent years. Among kinds of reconstruction methods, regularized reconstruction is widely used, because it applies simple principle and unique solution. The regularization parameter plays an important role in reconstruction. If the parameter is too small, the noise will not be effectively restrained, conversely, the reconstruction result will become blurry. Therefore, a U-curve based reconstruction method is proposed, using the unique features of U-curve to select the regularization parameter. The data fidelity term and a prior item are used to form a U-curve function, and the left maximum curvature point is selected as the optimal regularization parameter. The proposed method is tested on two simulate data sets. The results show advantages of this revised method both in visual effects and quantitative evaluation.