改进块匹配与初始估计的凸集投影图像重建

Improved Block-Matching and Initial Estimation for POCS Image Reconstruction

凸集投影算法进行图像超分辨率重建时,图像配准和初始估计对重建结果有重要影响。为解决传统块匹配算法小块匹配易受噪声干扰,大块匹配对运动对象跟踪不精确的问题,本文以空间十字频率法判断图像的平坦程度,根据图像平坦度进行匹配块的分割,实现了匹配块大小的动态选择;针对单帧初始估计包含信息量少的不足,将所有已知低分辨图像与插值图像配准,以最优插值法作用后的插值图像作为初始估计。实验证明,改进的配准算法消除了固定块配准方式在噪声和局部运动同时存在时的不足,且算法简单,运算量较小;新的初始估计生成方式则提高了初始估计的峰值信噪比,加快了重建的收敛速度。

In the process of reconstructing super-resolution images by POCS algorithm, image registration and initial estimation have important influence on the result of reconstruction. For solving the problem that the small block matching is liable to be interfered by noise and the big block matching can not track the local moving objects accurately, a method is provided to determine the spatial flatness of the image and divide the blocks according to the flatness of the image. It can change the block size dynamically. As the initial estimation for the single frame is lack of enough information, all known low-resolution images are used to register the interpolated image. Then the initial estimation is generated by the optimal interpolation method. Experiments show that the improved matching algorithm reduces the influence of noise and local motion on block-matching accuracy and it is also simple. Furthermore, the new algorithm improves the PNSR of the initial estimation and speeds up the convergence.