融合几何变换相似块的序列图像超分辨率重建

摘要

Similar pixels or image patches exist in different position inside an image or between different images, so that the fusion of similar patches can reconstruct the high resolution image. In order to mine the potential similar information further for the better reconstruction quality, the paper utilizes of the geometry transformation self-similarity of images, by extracting Zernike moments features within the image neighborhood to match and measure the nonlocal similar patches among the images. Then a super-resolution reconstruction algorithm for image sequences is proposed, through the collaborative weighting fusion of similar patches with geometric transformation. The experimental results prove the validity of the proposed method and its superiority in reconstruction effect.%图像内部不同位置或不同图像之间包含有相似的像素或图像分块，通过对相似块的融合可以完成高分辨率图像的重建。为了充分挖掘图像中潜在的相似信息，以提高重建质量，利用图像的几何变换自相似性，提取图像的邻域 Zernike 矩特征进行非局部相似性测度，提出一种融合几何变换相似信息的联合加权的序列图像超分辨率重建算法。实验结果验证了本文方法的有效性，与同类方法相比，本文方法具有更好的重建效果。