基于预测梯度的图像插值算法

Image Interpolation With Predicted Gradients

提出一种新的非线性图像插值算法,称为基于预测梯度的图像插值(Image interpolation with predicted gradients,PGI).首先沿用现有的边缘对比度引导的图像插值(Contrast-guided image interpolation,CGI)算法思想对低分辨率图像中的边缘进行扩散处理,然后预测高分辨率图像中未知像素的性质,最后对边缘像素采用一维有方向的插值,对非边缘像素采用二维无方向的插值.与通常的非线性图像插值算法相比,新算法对图像边缘信息的理解更为完善.与CGI算法相比,由于梯度预测策略的使用,PGI算法能够更有效地确定未知像素的相关性质(是否为边缘像素,以及是边缘像素时其边缘方向).实验结果表明,PGI算法无论在视觉效果还是客观性测评指标方面均优于现有的图像插值算法.此外,在对彩色图像进行插值时,本文将通常的RGB颜色空间转化为Lab颜色空间,不仅减少了伪彩色的生成,而且降低了算法的时间复杂度.

A new nonlinear image interpolation algorithm is proposed, referred to as image interpolation with predicted gradients（PGI）. First, the idea of contrast-guided image interpolation（CGI） is employed to diffuse the edges in the lowresolution（LR） image. Then, unknown pixels in the high-resolution（HR） image are predicted. Finally, a 1-D directional filter is employed to process edge pixels while a 2-D directionless filter is used to interpolate non-edge pixels. Compared to the common nonlinear image interpolation algorithms, the new algorithm has a better interpretation of image edges.Compared to the CGI, the PGI can predict the property of unknown pixels more precisely（including whether an unknown pixel is an edge pixel or not, and its direction if it is）. Experimental results show that PGI has a better performance than the existing algorithms, either with respect to visual effect or in terms of objective criteria. In addition to interpolate color images, the usual RGB space needs to be converted to the Lab space. As a result, pseudo-color can be suppressed and the computational complexity is reduced.