Study & Analysis of Effects of Deblurring on Different Sets of Images

To recover a sharp version from a blurred image is a long standing inverse problem. Although an image can be deblurred with any kind of deblurring technique. But type of image also matters a lot. In this paper, we analyzed the research on this topic experimentally through three paradigms: 1) the normal image; 2) average image; and 3) dense image. Firstly we blurred our image with Gaussian blur and Motion blur. Then, we will try to deblur the blurred image with basic deblurring techniques, that is Blind Deconvolution, Lucy Richardson Algorithm, Wiener Filter and Regularized Filter. All these filters have their own effects on the blurred image.

[1]  Liangpei Zhang,et al.  A Blind Restoration Method for Remote Sensing Images , 2012, IEEE Geoscience and Remote Sensing Letters.

[2]  Guangcan Liu,et al.  Blind Image Deblurring Using Spectral Properties of Convolution Operators , 2014, IEEE Transactions on Image Processing.

[3]  William A. Sethares,et al.  Blind deconvolution of noisy blurred images via dispersion minimization , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[4]  Laure Blanc-Féraud,et al.  Sparse Poisson Noisy Image Deblurring , 2012, IEEE Transactions on Image Processing.

[5]  Ming Jiang,et al.  Blind deblurring of spiral CT images , 2003, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[6]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, SIGGRAPH 2008.

[7]  P. Govardhan,et al.  Improvement of Image Deblurring Through Different Methods , 2013 .

[8]  Wei Xiong,et al.  Rotational Motion Deblurring of a Rigid Object from a Single Image , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[9]  Lei Zhang,et al.  Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization , 2010, IEEE Transactions on Image Processing.