In this paper, we consider the problem of restoring blurred noisy vectorial images where the blurring model involves contributions from the different image channels (cross-channel blur). The proposed method restores the images by solving a sequence of quadratic constrained minimization problems where the constraint is automatically adapted to improve the quality of the restored images. In the present case, the constraint is the Total Variation extended to vectorial images, and the objective function is the 2 norm of the residual. After proving the convergence of the iterative method, we report the results obtained on a wide set of test images, showing that this approach is efficient for recovering nearly optimal results.
Chen, K., Loli Piccolomini, E., Zama, F. (2016). Iterative Constrained Minimization for Vectorial TV Image Deblurring. JOURNAL OF MATHEMATICAL IMAGING AND VISION, 54(2), 240-255 [10.1007/s10851-015-0599-3].
Iterative Constrained Minimization for Vectorial TV Image Deblurring
LOLI PICCOLOMINI, ELENA;ZAMA, FABIANA
2016
Abstract
In this paper, we consider the problem of restoring blurred noisy vectorial images where the blurring model involves contributions from the different image channels (cross-channel blur). The proposed method restores the images by solving a sequence of quadratic constrained minimization problems where the constraint is automatically adapted to improve the quality of the restored images. In the present case, the constraint is the Total Variation extended to vectorial images, and the objective function is the 2 norm of the residual. After proving the convergence of the iterative method, we report the results obtained on a wide set of test images, showing that this approach is efficient for recovering nearly optimal results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
