局部自适应非线性图像放大算法

A Locally-Adaptive Nonlinear Image Zooming Algorithm

已有的基于相关性检测的图像缩放算法所检测的边缘方向较少,并且只考虑了边缘特征的常数相关性,有时难以发掘像素之间所蕴含的关联性.为此,提出一种基于相关性检测的非线性图像放大算法.在检测图像中像素的相关性时,充分考虑像素之间潜在的相关方向;在检测相关性的过程中引入针对图像边缘特征的线性和二次相关性检测,用常数、线性或者二次函数来拟合像素之间的关系,从而更全面地描述像素之间的相关性,即图像的边缘特征,使得图像在放大过程中沿着图像的边缘特征进行,有效地消除了放大图像的模糊问题.实验结果表明,该算法具有良好的局部性,适合于GPU并行实现.

Since current image resizing algorithms via correlation detection do not fully consider the correlation directions and only adopt the constant correlation along the detected image edge,they cannot always reveal the potential correlations among the neighboring pixels.An algorithm of non-linearly zooming in an image is proposed,which is based on the correlation detection.The potential correlation directions are fully considered;and constant,linear and quadric correlations along the image edges are detected,which can describe local image edge features better.Experimental results show that the proposed algorithm can alleviate the image blur and accomplish high quality zooming-in effects.Furthermore it is a local algorithm and can be implemented in parallel on GPU.