Deblurring of color images corrupted by impulsive noise.
We consider the problem of restoring a multichannel image corrupted by blur and impulsive noise (e.g., salt-and-pepper noise). Using the variational framework, we consider the L1 fidelity term and several possible regularizers. In particular, we use generalizations of the Mumford-Shah (MS) functional to color images and gamma-convergence approximations to unify deblurring and denoising. Experimental comparisons show that the MS stabilizer yields better results with respect to Beltrami and total variation regularizers. Color edge detection is a beneficial by-product of our methods.
Bar
Leah
L
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. barxx002@umn.edu
Brook
Alexander
A
Sochen
Nir
N
Kiryati
Nahum
N
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity