超分辨率图像重建是增强那些低成本成像传感器系统图像分辨率的有效措施.得益于先验知识的学习,低分辨率图像可有效地被超分辨率增强.针对带有明显边缘结构的图像,现有方法没有有效利用高阶信息从而会出现一些光滑的图像细节.本文针对这种特殊的图像结构,研究一种基于二阶广义方向性全变分的重建方法来挖掘那些隐含的高阶可利用信息.二阶广义方向性全变分不仅可以作为先验知识,还能作为稀疏正则项抑制伪影和噪声.实验结果表明,本文方法可有效超分辨率重建结构边缘图像,并可获得高分辨率图像细节和纹理特征.%Super-resolution (SR) image reconstruction has developed into a powerful tool to enhance the image resolution for the systems with low-cost imaging sensors.A direct but efficient approach to super-resolve a low-resolution image is based on prior knowledge learning.But the existing methods do not consider matched high-level features in the images with structured edges,resulting in some smooth image artifacts.A second-order directional total generalized variation (DTGV) regularization based method is proposed to explore the underlying high-level information of the data in this paper.More specifically,second-order DTGV acts as not only an additional prior but also an effective constraint to reduce the image artifacts and remove the noise.Results from several texture images demonstrate that the proposed approach can generate highresolution image details and tend to produce high-frequency textures.
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