自适应超分辨率图像的重建算法（英文）

背景：超分辨率重建已经在视频、遥感等许多领域内的到广泛的研究与应用。目的：介绍一种自适应超分辨率重建算法,以期从序列低分辨率图像中重建出高分辨率图像。方法：采用常数λ=2/3作为正则化参数和自适应步长作为第一种方案。第二种方案充分考虑到低分辨率图像中的运动误差估计、点扩散函数以及加性高斯白噪声对重建算法的影响。实验构造出新的非线性自适应正则化函数,进而利用实验方法分析代价函数的凸性。通过数学理论,根据代价函数凸性实验得到自适应步长因子,从而改进了图像的空间分辨率和算法的收敛速度。结果与结论：为验证此算法的有效性,采用光学图像进行实验。方案二图像峰值信噪比增高,其收敛速度为方案一的2倍以上;方案二的平均计算需要的时间为68.25s。结果证实,自适应超分辨率图像重建算法对图像分辨率和迭代的收敛速度均改善显著,其稳定性较好。

Adaptive algorithm of super-resolution image reconstruction

BACKGROUND： Super-resolution reconstruction has been extensively studied and used in many fields, such as video and remote sensing. OBJECTIVE： To reconstruct a high-resolution image from the low-resolution image sequence, an adaptive algorithm of super-resolution reconstruction is proposed. METHODS： We use a constant regularize parameter （λ=2/3） and adaptive step size as scheme Ⅰ. The scheme Ⅱ takes into account inaccurate estimates of the registration parameter, the point spread function and the additive Gaussian noise in the low resolution image sequence. We structure a novel adaptive regularization functional, and analyze experimentally the convexity of the nonlinear cost function. Based on the convex of the cost function, we get the adaptive step size by the mathematical theory, which improves the spatial resolution of the image and the rate of convergence. RESULTS AND CONCLUSION： Optical images are used to test the proposed method. The scheme Ⅱ performs better than scheme Ⅰ, in the sense of the enhanced peak signal to noise ratio. Compared to scheme Ⅱ, the computational cost of scheme Ⅰ is twice or more slower. The average computational cost of scheme Ⅱ is 68.25 seconds. The results show that the spatial resolution of the image and the rate of convergence are significantly improved. The experiment also proves that the stability of the algorithm is good with the progress of the iterative process.