Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images
Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images
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DOI：
10.1109/83.650118
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发表时间：
1997-12-01
影响因子：
10.6
通讯作者：
Feuer, A
中科院分区：
文献类型：
--
作者：
Elad, M;Feuer, A
单图像恢复理论中的三个主要工具是最大似然（ML）估计器、最大后验概率（MAP）估计器和使用凸集投影的集合论方法（POCS）。本文利用上述已知的工具，提出了一个统一的方法，对更复杂的问题的超分辨率恢复。在超分辨率恢复问题中，从多幅几何变形、模糊、噪声和下采样的测量图像中恢复出一幅提高分辨率的图像。从ML、MAP和POCS的角度对超分辨率恢复问题进行了建模和分析，得到了已知超分辨率恢复方法的推广。所提出的恢复方法是一般的，但假设明确的知识的线性空间和时间变化的模糊，（加性高斯）噪声，不同的测量分辨率，和（平滑）梅尔离子特性。提出了一种结合ML的简单性和非椭球约束的混合方法，与ML和POCS方法相比，该方法具有更好的恢复性能。证明了该混合方法收敛于一个新定义的最优化问题的唯一最优解，并讨论了静止测量的超分辨率恢复问题。仿真结果表明所提出的方法的力量。
The three main tools in the single image restoration theory are the maximum likelihood (ML) estimator, the maximum a posteriori probability (MAP) estimator, and the set theoretic approach using projection onto convex sets (POCS). This paper utilizes the above known tools to propose a unified methodology toward the more complicated problem of superresolution restoration. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, noisy and downsampled measured images, The superresolution restoration problem is modeled and analyzed from the ML, the MAP, and POCS points of view, yielding a generalization of the known superresolution restoration methods. The proposed restoration approach is general but assumes explicit knowledge of the linear space-and time-variant blur, the (additive Gaussian) noise, the different measured resolutions, and the (smooth) mel-ion characteristics. A hybrid method combining the simplicity of the ML and the incorporation of nonellipsoid constraints is presented, giving improved restoration performance, compared with the ML and the POCS approaches. The hybrid method is shown to converge to the unique optimal solution of a new definition of the optimization problem, Superresolution restoration from motionless measurements is also discussed. Simulations demonstrate the power of the proposed methodology.