激光与光电子学进展, 2018, 55 (4): 041003, 网络出版: 2018-09-11   

基于原始-对偶算法的自适应加权广义全变差图像去模糊 下载: 1152次

Adaptive Weighted Generalized Total Variation Image Deblurring Based on Primal-Dual algorithm
作者单位
天津大学电气自动化与信息工程学院, 天津 300072
摘要
传统全变差(TV)正则化图像复原仅考虑图像的一阶梯度特征,具有图像噪声敏感、平坦区域阶梯效应明显等缺点。针对此类问题,将广义全变差(TGV)应用于图像去模糊领域,提出自适应加权的TGV图像去模糊模型,该模型能够根据图像局部结构自适应调整权值,在去模糊的同时避免阶梯效应,有效保持图像边缘并抑制噪声。提出基于原始-对偶的自适应加权TGV去模糊模型的迭代求解算法,实验结果表明,利用本文算法可获得高质量复原图像,且时间复杂度低,求解速度快。
Abstract
In order to overcome the limitations of traditional total variation (TV) regularization in image restoration only considering the first-order gradient characteristic of the image with the deficient ability of detail recovery and sensitivity to the noise, the total generalized variation (TGV) is applied into image deblurring. An adaptive weighted TGV image deblurring model is proposed, which can adaptively adjust the weights according to the local image structure, avoiding the staircase effect while preserving the edges of the image and suppressing the noise. In order to solve the proposed model, the adaptive weighted TGV is proposed based on primal-dual algorithm. The experimental results show that our method can obtain high quality recovery images and the solving algorithm has low time complexity and fast solving speed.

杨爱萍, 张越, 王金斌, 何宇清. 基于原始-对偶算法的自适应加权广义全变差图像去模糊[J]. 激光与光电子学进展, 2018, 55(4): 041003. Aiping Yang, Yue Zhang, Jinbin Wang, Yuqing He. Adaptive Weighted Generalized Total Variation Image Deblurring Based on Primal-Dual algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041003.

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