A SVM-Based Blur Identification Algorithm for Image Restoration and Resolution Enhancement
Blur identification is usually necessary in image restoration. In this paper, a novel blur identification algorithm based on Support Vector Machines (SVM) is proposed. In this method, blur identification is considered as a multi-classification problem. First, Sobel operator and local variance are used to extract feature vectors that contain information about the Point Spread Functions (PSF). Then SVM is used to classify these feature vectors. The acquired mapping between the vectors and corresponding blur parameter provides the identification of the blur. Meanwhile, extension of this method to blind super-resolution image restoration is achieved. After blur identification, a super-resolution image is reconstructed from several low-resolution images obtained by different foci. Simulation results demonstrate the feasibility and validity of the method.
KeywordsSupport Vector Machine Support Vector Regression Point Spread Function Training Image Image Restoration
Unable to display preview. Download preview PDF.
- 3.Gevrekci, M., Gunturk, B.K.: Image Acquisition Modeling for Super-Resolution Reconstruction. IEEE International Conference on Image Processing(ICIP) 2, 1058–1061 (2005)Google Scholar
- 4.Robertson, M.A.: High-Quality Reconstruction of Digital Images and Video from Imperfect Observations. Ph.D thesis (2001)Google Scholar
- 6.Li, D., Mersereau, R.M., Simske, S.: Blind Image Deconvolution Using Support Vector Regression. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 113–116 (2005)Google Scholar
- 8.Chang, C.-C., Lin, C.-J.: LIBSVM: a Library for Support Vector Machines (2001), Available:http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 9.Mann, S., Mann, R.: Quantigraphic imaging: Estimating the camera response and exposures from differently exposed images. IEEE International Conference on Computer Vision and Pattern Recognition 1, 842–849 (2001)Google Scholar