Space-varying restoration of optical images
Space-varying restoration of optical images
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DOI：
10.1364/josaa.14.003162
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发表时间：
1997-12
影响因子：
1.9
通讯作者：
J. Nagy;V. P. Pauca;R. Plemmons;T. Torgersen
中科院分区：
文献类型：
--
作者：
J. Nagy;V. P. Pauca;R. Plemmons;T. Torgersen
光学图像质量的改进现在通常在两个阶段中尝试。第一阶段涉及自适应光学中的技术，并在观察到的图像最初形成时发生。提高光学图像质量的第二阶段通常离线进行，包括图像恢复的后处理步骤。图像恢复是一个不适定的逆问题，涉及去除或最小化由图像中的噪声和模糊引起的退化，在这种情况下，通过介质成像。我们的工作涉及一个新的空间变化的正则化方法和相关技术，用于加速迭代图像后处理计算的收敛。去噪方法，包括总变差最小化，其次是基于分割的预处理方法的最小残差共轭梯度迭代，进行了研究。正则化是通过将图像分割成（平滑）段并在段之间改变预处理器来完成的。该方法似乎特别适用于分段平滑的图像。我们的算法的计算复杂度仅为O（ln2 log n），其中n2是图像中的像素数，l是所使用的分段数。此外，并行化是直接的。数值试验的模拟和实际的大气成像问题的报告。与不使用分割的情况下进行比较。结果发现，我们的方法是特别有吸引力的低信噪比恢复图像，并有效地抑制了噪声放大的迭代，导致一个数值上有效的和强大的正则化迭代恢复算法。
The improvement in optical image quality is now generally attempted in two stages. The first stage involves techniques in adaptive optics and occurs as the observed image is initially formed. The second stage of enhancing the quality of optical images generally occurs off line and consists of the postprocessing step of image restoration. Image restoration is an ill-posed inverse problem that involves the removal or the minimization of degradations caused by noise and blur in an image, resulting from, in this case, imaging through a medium. Our work concerns a new space-varying regularization approach and associated techniques for accelerating the convergence of iterative image postprocessing computations. Denoising methods, including total variation minimization, followed by segmentation-based preconditioning methods for minimum residual conjugate gradient iterations, are investigated. Regularization is accomplished by segmenting the image into (smooth) segments and varying the preconditioners across the segments. The method appears to work especially well on images that are piecewise smooth. Our algorithm has computational complexity of only O(ln2 log n), where n2 is the number of pixels in the image and l is the number of segments used. Also, parallelization is straightforward. Numerical tests are reported on both simulated and actual atmospheric imaging problems. Comparisons are made with the case where segmentation is not used. It is found that our approach is especially attractive for restoring images with low signal-to-noise ratios, and that magnification of noise is effectively suppressed in the iterations, leading to a numerically efficient and robust regularized iterative restoration algorithm.