频域内基于邻域特征学习的单幅图像超分辨重建

Single-image Super-resolution Reconstruction in Frequency Domain Based on Neighborhood Feature Learning

针对图像重建过程中待插值点灰度估计不准确的问题,提出一种基于邻域特征学习的单幅图像超分辨回归分析方法。在输入低分辨率图像后,利用图像特征从低分辨率图像及其对应高分辨率图像的几何相似结构中学习局部协方差。对于邻域中的每一个图像块,估计4个方向的方差以适应插值像素。实验结果表明,该方法既能保证重建的高分辨率图像均匀区域的一致性,同时也能完整保留图像细节信息和边缘轮廓。

Aiming at the problem that the interpolation point gray estimation is not accurate in the process of image reconstruction,a single-image super-resolution algorithm in frequency domain based on neighborhood feature learning is proposed in this paper. When giving a low-resolution image as input,it uses image feature to learn local covariance from low-resolution image and its corresponding high-resolution image＇ s geometric similar structure. For each patch in the neighborhood, four directional variances are estimated to adapt the interpolated pixels. Experimental results demonstrate that the proposed method not only can guarantee the consistency of the smooth region in the reconstructed high-resolution image, but also can retain the image details and the integrity of the edge profile.