Research For Regularized Super-resolution Image Reconstruction Algorithm Based On M-estimation

Super Resolution Reconstruction is becoming a research hotspot in image processing area. It refers to methods that utilize information from multiple low resolution observed images to achieve restoration at resolution higher than that of original data. It is a kind of information fusion technologies that sacrifices time band for spatial resolution.The fundamental of super resolution reconstruction is presented along with degradation process modeling and regularized super resolution reconstruction algorithm. A detailed introduction to image quality assessment is also provided. The robust performance of an algorithm is very important for practical use. Existing algorithm based on least square estimator is examined to understand the limitations of robust characteristics. A cost function using M-estimators in regularized framework, which has better robust performance, is introduced. Then a comparison of common M-estimators is presented from two aspects, Huber estimator is proposed to use in cost function for its robust characteristic, estimation with small bias and convexity. And median absolute deviation is used for computing scale parameters of Huber estimator, which avoids error caused by artificial selection. Experiment results demonstrate that the proposed method has better performance than existing algorithms.Regularized term plays an important role in accelerating the convergence and stabilizing solution. It also affects visual effect of processing images, such as edge-preserving and so on. The design of regularized term for better visual perception is also a research hotspot in SR reconstruction. Based on bilateral filter and M-estimation, a novel regularized term is proposed. Because of combination of robustness of M-estimation and double weighting idea of bilateral filter, hence behaves much better in edge-preserving. Bilateral Total Variation is widely used in existing algorithms for its good noise-suppressing and edge-preserving ability, but it has"staircase effect"when suffering from severe noise. Then selection of M-estimator is examined for reducing"staircase effect". Several experiments are designed to verify the effectiveness of this new regularized term using Huber estimator.