基于非线性最小二乘的图像自适应SR重建以及运动估计

图像超分辨率（SR）重建是利用数字信号处理技术由一系列低分辨率观测图像得到高分辨率图像。为了扩展SR技术的应用范围，提出了一种同时进行图像超分辨率重建和全局运动估计的方法。该方法首先基于最大后验概率(MAP)给出了图像SR重建和运动估计框架，该框架不仅考虑了前后两次迭代所得的HR图像差值对最终重建图像的影响，而且引入了不同LR图像对重建图像的重要性权值，使得算法具有自适应性；然后将总体框架转换为图像SR重建模型和运动估计模型；最后基于非线性最小二乘法对模型进行优化求解，得出了SR重建图像及其全局运动域。实验表明，该方法不仅图像重建效果良好，并有着良好的收敛性。

An adaptive method for simultaneous image super-resolution and motion estimation based on nonlinear least square

Image super-resolution(SR) reconstruction refers to a signal processing approach which produces high-resolution images from observed multiple low-resolution images A new method for simultaneous image super-resolution and motion estimation is proposed to expand the application range of SR technology. The framework of SR resolution and motion estimation is given based on maximum a posteriori (MAP). The framework takes into account both the influence of HR image dispersion between two iterations, and the weight of different LR images, which makes the algorithm self-adapting. The framework then can turn to SR resolution and motion estimation model. Nonlinear least squares method is employed to solve the model to get the global motion area of SR resolution. Our experimental results show the effectiveness of the proposed algorithm.