Advances in Computer Science and Information Technology. Computer Science and Information Technology. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part III

Research Article

Image Deblurring Using Bayesian Framework

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  • @INPROCEEDINGS{10.1007/978-3-642-27317-9_52,
        author={K Sitara and S. Remya},
        title={Image Deblurring Using Bayesian Framework},
        proceedings={Advances in Computer Science and Information Technology. Computer Science and Information Technology. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part III},
        proceedings_a={CCSIT PART  III},
        year={2012},
        month={11},
        keywords={Image Restoration point spread function image deblurring},
        doi={10.1007/978-3-642-27317-9_52}
    }
    
  • K Sitara
    S. Remya
    Year: 2012
    Image Deblurring Using Bayesian Framework
    CCSIT PART III
    Springer
    DOI: 10.1007/978-3-642-27317-9_52
K Sitara1,*, S. Remya1,*
  • 1: University of Kerala
*Contact email: sitarak1987@gmail.com, remyayes@gmail.com

Abstract

This paper proposes a new method for identifying the blur model and its parameters to restore the image from the blurred image. This is based on the specific distortions caused by the distorting operator in the Fourier spectrum amplitude of an image. Due to the ill-posed nature of image restoration (IR) process, prior knowledge of natural images is used to regularize the IR problem. The Bayesian approach provides the means to incorporate prior knowledge in data analysis. The choice of prior is very important. A comparative analysis using various priors was studied qualitatively. The sparse and redundant prior method gives better results both subjectively and objectively when compared with other priors.