基于轮廓模板和自学习的图像纹理增强超采样算法

提出一种以轮廓模板插值和局部自学习相结合的图像纹理增强超采样算法，有效地恢复了插值图像丢失的细节纹理，抑制了插值图像边缘的扩散.该方法通过局部自相似性在原始低分辨图像中估计高频信息，对轮廓模板插值图像的细节纹理进行了恢复.其中，为了弥补轮廓模板插值缺少先验知识的缺陷，将原始低分辨率图像的高频信息作为先验知识.为了保证估计的高频信息最优，匹配的过程中采用双匹配，相比较于全局搜索和小窗搜索，提高了效率并保证了匹配精度.此外，使用高斯模糊代替了传统提取高频信息的方法，简化了算法的复杂度，提高了准确性和效率.对估计得到的高频信息采用高斯函数加窗，以减小估计出错和重叠区的混叠影响.本文算法的训练库由原始低分辨图像自身和插值图像构成，节省了生成训练库所需的时间和空间.训练库的简化使得高频信息的估计可以多尺度进行，算法效率得到进一步优化.理论分析和实验结果表明，相比传统的基于插值、基于自学习的图像超分辨率方法，本文方法获得更好的实验结果，主观效果得到明显改善，有效地恢复了图像的纹理细节，提高了图像边缘锐度，避免了产生锯齿等人工效应，客观指标得到提高.

Image Texture Enhancement via Upscaling Algorithm Based on Contour Stencils and Self-learning

An image texture enhancement via upscaling algorithm based on interpolation of contour stencils and local self-learning is proposed in this paper, by which the lost detail texture of the interpolated image is effectively recovered and the edge diffusion in image magnification is decreased. In this method, the detail texture of the interpolated image is estimated by local self-similarity in the original low-resolution image. To make up the lack of prior knowledge of the interpolation algorithm, the high frequency information of original low-resolution image works as a priori knowledge. In order to estimate the best high-frequency information, the use of dual matches improves efficiency and accuracy, compared to the global search and the small window search. Instead of the traditional high frequency information extraction method, Gaussian blur is used to reduce the complexity of the algorithm. In addition, adding window on the estimated high-frequency information by Gaussian function inhibits the aliasing effect of overlapping area, as well as reduces the influence of the estimation error. To save space and time, the training library in this paper consists of original low-resolution image and the interpolated image. Simplifying training library makes high-frequency information estimated in a single multi-scale, so the algorithm efficiency is further improved. Theoretical analysis and experimental results show that, the proposed method outperforms other comparing algorithms including the traditional interpolation algorithm and the related algorithms based on self-learning in terms of both objective and visual quality of the interpolation image. The method recovers the detail texture of the image effectively, and enhances the sharpness, also avoids producing the artificial such as sawtooth effect.