基于图像自相似性及字典学习的超分辨率重建算法

Image Super-resolution Reconstruction Algorithms Based on Self-similarities and Dictionary Learning

图像超分辨率重建技术在重构图像细节,改善图像视觉效果等方面起着重要作用。为了提高超分辨率图像的重构质量,本文结合图像自身和自然图像库信息进行超分辨率重建。先利用图像在不同尺度的自相似性,形成图像金字塔,只用单幅低分辨率图像进行超分辨率重建;然后利用自然图像库进行字典学习并以初步得到的重建图像作为输入再次处理;在图像后处理时,利用图像非局部相似性和迭代反投影,进一步提高重建效果。实验结果表明,本文的方法与其它几种基于学习的超分辨率算法比较,无论主观视觉效果上还是峰值信噪比上都有明显提高。

Super-resolution reconstruction plays an important role in reconstructing the image details and improving the visual perception.In the most of the conventional learning-based super-resolution,prior knowledge of the input image itself or natural images database is used to solve the super-resolution problem,so the quality of reconstructed images can be further improved.To reach this goal,the information of the image itself and the natural images database are combined.Firstly,the self-similarities across different image scales can be exploited to construct an image pyramid,and the high resolution image is reconstructed only by the input.After that,we learn a dictionary from natural image patches and reconstruct the initial reconstruction one,which is regarded as the input.In the back processing,non-local similarity and iterative back-projection are exploited to further improve the quality.The experiments show that the proposed algorithm achieves better results than other learning-based algorithms in terms of both visual perception and peak signal-to-noise ratio.