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气动畸变退化图像复原方法研究
Alternative TitleResearch on degraded image restoration under aero-optic effect
胡文锐
Subtype工学博士
Thesis Advisor张文生
2014-05-28
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword气动畸变退化效应 非刚体图像配准 非局部稀疏编码 张量分解 可调整核回归 Aero-optic Effect Nonrigid Image Registration Nonlocally Sparse Coding Tensor Decompostion Steer Kernel Regression
Abstract在大气湍流动力环境下,由于湍流场密度不均匀且随机扰动,远距离成像探测系统获取的单帧图像呈随机的畸变和空间可变的模糊效应,序列图像呈抖动效应。该畸变退化现象严重影响探测系统对目标的检测、识别、定位与跟踪,需要利用图像复原方法对受大气湍流效应影响的畸变退化图像进行预处理从而获得高质量图像。然而,畸变退化图像同时存在密集的非刚体几何畸变、高度随机的时空可变模糊,以及传感器噪声干扰,复合退化效应复原具有严重的病态性,缺少深入的理论基础和有效的技术支撑。本文研究气动畸变退化图像复原方法,具有重要的理论意义和广泛应用前景。 本文分别进行非刚体图像配准、图像去噪和图像融合理论与方法的研究,用以解决气动畸变退化图像复原问题。首先,耦合有参形变模型和无参形变模型,分别提出了基于全局总变分的非刚体图像配准算法(TVRC)与基于L1 耦合正则化的非刚体图像配准算法(TVL1),用以校正非刚体几何畸变;其次,结合非局部稀疏编码和张量分解技术,分别提出了基于张量分解和均方差迭代最优的图像去噪算法(iHOSVD)与基于非局部稀疏编码和张量分解的图像去噪算法(NLSCTD),用以去除传感器噪声干扰;最后,利用非刚体图像配准、参考图像复原以及核回归方法融合畸变退化图像序列,提出了一种基于变分模型和形变场信息的气动畸变退化图像复原算法,用以处理非刚体几何畸变和时空可变模糊复合退化问题。 本文主要工作和贡献如下: 1.针对气动畸变退化图像中存在几何畸变的问题,分别提出了TVRC非刚体图像配准算法与TVL1非刚体图像配准算法。两种算法共同之处在于均考虑到有参形变模型和无参形变模型的优缺点,并将两种形变模型耦合,同时利用全局总变分作为形变场的泛函约束。不同之处在于,TVRC算法采用基于L2范数的耦合形式和一种简单高效的两阶段方法进行优化求解,而TVL1 算法利用L1范数对TVRC 算法的耦合形式进行改进并采用有参形变和无参形变交替优化的策略得到更加精确的配准结果。由于结合了有参形变模型对噪声的全局鲁棒性和无参形变模型对形变局部描述的精确性,因此在有噪声干扰的情况下,TVRC算法和TVL1 算法既能够保持图像整体结构又能够调整高度局部化形变。将提出算法在仿真和真实数据集上与四种国际先进非刚体图像配准算法进行对比,实验结果表明新的算法在视觉效果和数值精度(均方差和互信息指标)上优于其它算法。 2.针对远距离成像探测系统存在噪声干扰的问题,分别提出了iHOSVD图像去噪算法与NLSCTD图像去噪算法。iHOSVD算法利用基于Tucker张量分解的非局部协同滤波技术迭代地去除噪声,并证明了该迭代是权衡偏差和方差使均方差达到最优的过程。NLSCTD算法将非局部稀疏编码技术和Tucker张量分解技术有机的融合到一个贝叶斯框架下,并通过分裂Bregman迭代对图像进行去噪。iHOSVD算法和NLSCTD算法均通过迭代协同滤波形式滤除噪声,不仅能够有效去除噪声,同时能够保持图像纹理细节。与iHOSVD算法相比,由于利用非局部稀疏编码构建图像结构部分的约束,NLSCTD算法对平滑图像具有更好的去噪效果。将提出算法在通用数据集上与BM3D、NCS...
Other AbstractDue to the effect of atmosphere turbulence, the image or video from long-range imaging system is geometrically distorted, blurred, jittered and noised, which makes the detection, recognition, location and tracking of the target difficult. Therefore, removing the turbulence effect for high-quality target image is important. However, restoring the degraded image is challenging and one of the biggest challenges is the coexistence of dense nonrigid geometrically distortions, space-time varying blurs and sensor noise. In this work, we research the restoration of the degraded image under aero-optic effect, which has important theoretical significance and broad application prospects. We use nonrigid image registration, image denoising and image fusion to solve the challenging image restoration problem. Firstly, we couple the parametric and the non-parametric transformation model, and propose two nonrigid image registration algorithms. Secondly, we combine nonlocally sparse coding and tensor decomposition, and propose two image denoising algorithms. Finally, we unify nonrigid image registration, image denoising and image fusion and propose a restoration algorithm based on variation model and deformation information. The details and contributions are: 1.In order to correct the geometrically distortions, we propose two accurate and robust nonrigid image registration algorithms, namely, TVRC algorithm and TVL1 algorithm. Both of two algorithms combine the parametric transformation model and the nonparametric transformation model, and take the total variation as the constraint of transformation field, which make the two algorithms not only can be robust to the noise but also can align the highly-localized distortions. The difference between TVRC and TVL1 is that: TVRC uses the L2 norm to combine the two different transformation models and a two-stage strategy to optimize the objective function while TVL1 uses the L1 norm and an alternative optimizing strategy to get more accurate registration results. Experiments compared with other well-know nonrigid image registration algorithms illustrate that the proposed two algorithms can capture the local details of transformation accurately and effectively while be robust to noise. 2.In order to remove the effect of sensor noise, we propose two texture-preserved image denoising algorithms, namely, iHOSVD algorithm and NLSCTD algorithm. iHOSVD uses the nonlocally collaborative filtering technique base on Tucker tensor dec...
shelfnumXWLW2033
Other Identifier201118014629081
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6631
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
胡文锐. 气动畸变退化图像复原方法研究[D]. 中国科学院自动化研究所. 中国科学院大学,2014.
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