Iterative Constrained Minimization for Vectorial TV Image Deblurring
Iterative Constrained Minimization for Vectorial TV Image Deblurring
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DOI：
10.1007/s10851-015-0599-3
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发表时间：
2016-02-01
影响因子：
2
通讯作者：
Zama, F.
中科院分区：
文献类型：
--
作者：
Chen, K.;Piccolomini, E. Loli;Zama, F.
在本文中，我们考虑恢复模糊噪声矢量图像的问题，其中模糊模型涉及不同图像通道的贡献（跨通道模糊）。所提出的方法通过解决一系列二次约束最小化问题来恢复图像，其中约束自动适应以提高恢复图像的质量。在本例中，约束是扩展到矢量图像的总变分，目标函数是残差的范数。在证明迭代方法的收敛性后，我们报告了在大量测试图像上获得的结果，表明该方法可以有效地恢复接近最佳的结果。
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 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.