基于频谱预处理与改进霍夫变换的离焦模糊盲复原算法
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摘要
为了提高离焦模糊图像复原清晰度,提出一种基于频谱预处理与改进霍夫变换的离焦模糊盲复原算法。首先改进模糊图像频谱预处理策略,降低了噪声对零点暗圆检测的影响。然后改进霍夫变换圆检测算法,在降低算法复杂度的同时,增强了模糊半径估计的准确性。最后利用混合特性正则化复原图像模型对模糊图像进行迭代复原,使复原图像的边缘细节更加清晰。实验结果表明,提出的模糊半径估计方法较其他方法平均误差更小,改进的频谱预处理策略更有利于零点暗圆检测,改进的霍夫变换圆检测算法模糊半径估计精度更高,所提算法对已知相机失焦的小型无人机拍摄的离焦模糊图像具有更好的复原效果。针对离焦模糊图像复原,通过理论分析和实验验证了改进的模糊半径估计方法的鲁棒性强,所提算法的复原效果较好。
To improve the resolution of defocused blurred images, the paper presents a blind restoration algorithm of focus blur based on spectrum preprocessing and improved Hough transform. Firstly, the spectrum preprocessing strategy is improved to deal with the blur image spectrum map, and the impacts of noise on the detection of zero point dark circle is reduced. Then the algorithm of Hough transform circle detection is improved to reduce its complexity, meanwhile the accuracy of the estimation of the blur radius is enhanced. Finally, the authors take advantage of the mixed characteristic regularized restoration image model to restore the blur image iteratively, which makes the edge details of the restored image more focused. The experimental results show that the proposed method of blur radius estimation has less average error than other methods. The improved spectrum preprocessing strategy is more advantageous to the detection of zero dark circles. The improved algorithm of the Hough transformation circle detection algorithm has higher accuracy of the blur radius estimation. The proposed algorithm has a better recovery effect on defocused images taken by a small UAV out of focus on the camera. As for the blind restoration of focus blur images, the theoretical analysis and experiment validate the good robustness of the improved blur radius estimation method in this paper.
To improve the resolution of defocused blurred images, the paper presents a blind restoration algorithm of focus blur based on spectrum preprocessing and improved Hough transform. Firstly, the spectrum preprocessing strategy is improved to deal with the blur image spectrum map, and the impacts of noise on the detection of zero point dark circle is reduced. Then the algorithm of Hough transform circle detection is improved to reduce its complexity, meanwhile the accuracy of the estimation of the blur radius is enhanced. Finally, the authors take advantage of the mixed characteristic regularized restoration image model to restore the blur image iteratively, which makes the edge details of the restored image more focused. The experimental results show that the proposed method of blur radius estimation has less average error than other methods. The improved spectrum preprocessing strategy is more advantageous to the detection of zero dark circles. The improved algorithm of the Hough transformation circle detection algorithm has higher accuracy of the blur radius estimation. The proposed algorithm has a better recovery effect on defocused images taken by a small UAV out of focus on the camera. As for the blind restoration of focus blur images, the theoretical analysis and experiment validate the good robustness of the improved blur radius estimation method in this paper.
引文
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[2]尹诗白,王卫星,王一斌,等.贝叶斯迭代联合双边滤波的散焦图像快速盲复原[J].物理学报,2016,65(23):2342021-2342028.
[3]张阳.空间离焦模糊图像恢复处理技术研究[D].上海:中国科学院研究生院(上海技术物理研究所),2016.
[4]陈前荣,陆启生,成礼智,等.利用拉氏算子鉴别散焦模糊图像点扩散函数[J].计算机工程与科学, 2005,27(9):40-43.
[5]郑楚君,李榕,常鸿森.离焦模糊数字图像的Wiener滤波频域复原[J].激光杂志, 2004, 25(5):57-58.
[6]吴振宇,杜少军,姚洪利.采用聚焦度量的散焦模糊图像参数鉴别及复原[J].红外与激光工程,2011,40(4):772-776.
[7]梁敏,朱虹.基于边缘模糊频谱特征的散焦参数估计方法[J].计算机应用, 2014, 34(4):1177-1181.
[8]周箩鱼,张葆,杨扬.采用Hough变换的离焦模糊参数的估计[J].红外与激光工程,2012,41(10):2833-2837.
[9]赵明华,张鑫,石争浩,等.正弦积分拟合的图像复原边界振铃效应抑制方法[J].中国图象图形学报,2017, 22(2):249-256.
[10]李旭超,边素轩,李玉叶.图像恢复中的凸能量泛函正则化模型综述[J].中国图象图形学报,2016,21(4):405-415.
[11]唐述,谢显中.多正则化混合约束的模糊图像盲复原方法[J].电子与信息学报, 2015, 37(4):770-776.
[12]符颖,吴锡,周激流.基于多正则化约束的图像去运动模糊[J].四川大学学报:工程科学版,2017,49(3):123-128.