Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India.
IEEE Trans Image Process. 2012 Sep;21(9):4029-39. doi: 10.1109/TIP.2012.2201492. Epub 2012 May 25.
Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve the inverse problem using Bregman iterations. The proposed algorithm can suppress inherent noise generated during low-resolution image formation as well as during SR image estimation efficiently. Experimental results show the effectiveness of the proposed regularization and reconstruction method for SR image.
多尺度形态学算子在图像处理和特征提取领域的文献中得到了广泛的研究。在本文中，我们基于多尺度形态学为边缘保持型超分辨率（SR）图像重建建立了一种非线性正则化方法。我们将 SR 图像重建表述为一个去模糊问题，然后使用 Bregman 迭代法来求解这个反问题。所提出的算法可以有效地抑制在低分辨率图像形成过程中以及在 SR 图像估计过程中产生的固有噪声。实验结果表明，该正则化和重建方法对 SR 图像是有效的。
