小波域多角度轮廓模板变分模型的单幅图像超分辨率重建

近年来图像超分辨率重建技术因其可以提高图像的识别精度和识别能力而受到重视，其中一个难点问题是如何保证图像边缘纹理区域的重建质量.本文提出一种基于小波域的单幅图像超分辨率重建方法，首先对输入图像进行非下采样小波变换，根据小波变换的多方向性提出三类多角度模板，并采用TV模型估计各子带轮廓，确定其所属的最优方向，然后利用多角度模板来对各个子带进行双三次B样条插值，最后进行非下采样小波反变换.该方法使重建后图像的边缘、纹理信息更加精细，克服了诸如双线性插值法与双三次插值法等传统插值重建所产生的边缘模糊与边缘锯齿化，以及纹理区域失真等不足，在一定程度上提高了重建图像的质量.该方法可用于图像监控、遥感影像分析和医学图像处理等领域.大量的仿真实验验证了所提出方法的有效性.

Single Image Super-resolution Reconstruction Approach Based on Multi-angle Contour Templates Variational Calculus Model in Wavelet Domain

In recent years,the study of image super-resolution reconstruction technology has been paid much attention to,because it can improve image recognition accuracy and recognition ability.One of the difficult problems is how to ensure the reconstruction quality of image edge texture area.In this paper,a single image super-resolution reconstruction approach based on wavelet domain is proposed.Firstly,the non-subsampled wavelet transform (NSWT) is applied to the input image,according to the multi-directionality of wavelet transform,three kinds of multi-angle templates are proposed,and each subband contour is estimated by total variation model (TV model) to determine its optimal direction.Then,the multi-angle templates and bicubic B-spline interpolation are used to interpolate the subbands.Finally,the non-subsampled wavelet inverse transform is implemented.This approach makes edge information and texture information of the reconstructed images more precise,and overcomes some deficiencies such as edge blurring,edge serration,as well as distortion of texture region,caused by traditional interpolation reconstruction approaches,such as bilinear interpolation and bicubic interpolation,etc.The quality of reconstructed image is improved.This approach can be used in image monitoring,remote sensing image analysis,medical image processing,and so on.A large number of simulation experiments verify the effectiveness of the proposed approach.