Restoration of blurred images with conditional total variation method

Image deblurring based on a conditional total variation approach is presented. The restored image minimizes the total variation in the class of images which satisfy the constraints given by the Kronrod’s variations. Computer simulation results using a real image are provided and discussed.

[1]  J Flusser,et al.  Recognition of blurred images by the method of moments , 1996, IEEE Trans. Image Process..

[2]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Z. Grande.  On functions of two variables equicontinuous in one variable , 1996 .

[4]  Mostafa Kaveh,et al.  Blind image restoration by anisotropic regularization , 1999, IEEE Trans. Image Process..

[5]  Jan Flusser,et al.  Multichannel blind iterative image restoration , 2003, IEEE Trans. Image Process..

[6]  Tony F. Chan,et al.  Total variation blind deconvolution , 1998, IEEE Trans. Image Process..

[7]  R. Mersereau,et al.  Iterative methods for image deblurring , 1990 .

[8]  I. A. Ovseevich,et al.  Image Restoration Spectral Techniques , 2009 .

[9]  Dana H. Brooks,et al.  Electrical imaging of the heart , 1997, IEEE Signal Process. Mag..

[10]  Deepa Kundur,et al.  Blind Image Deconvolution , 2001 .

[11]  P. Lions,et al.  Image recovery via total variation minimization and related problems , 1997 .

[12]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[13]  Aggelos K. Katsaggelos,et al.  Digital image restoration , 2012, IEEE Signal Process. Mag..