In this paper we propose a blind super-resolution image restoration algorithm based on Support Vector Machines (SVM).Firstly

Sobel operator and local variance were used to extract feature vectors that contain information about different Point Spread Functions (PSFs) and SVM was used to classify these feature vectors.The acquired mapping between the vectors and corresponding blur parameters provided the identification of the blur.After blur identification

a super-resolution image was reconstructed from several low-resolution images obtained in different illumination conditions.The reconstructed image has high spatial resolution and dynamic range.We also propose a sub-pixel registration algorithm based on Retinex theory.Simulation results demonstrate the effectiveness and higher performance of the proposed method in both objective measurements and subjective visual quality.