双边全变分的自适应核回归超分辨率重建

正则化方法是目前解决超分辨率重建中病态问题的一种被广泛使用的方法。在分析了现有基于多种正则化超分辨率重建方法的基础上，构造了一种基于双边全变分（BTV）的自适应核回归滤波核，并将它作为正则化超分辨率重构的代价函数，该方法根据图像特征自适应生成正则项的滤波核函数。实验结果表明，与传统的正则化重建方法相比较，该算法既能有效地去除噪声，也能很好地保留图像细节部分，同时还具有一定的鲁棒性。通过客观和主观评价表明，图像重建质量有显著的提高。

Adaptive kernel regression super-resolution reconstruction based on bilateral total variation

Regularization method is used widely to solve the ill-posed problem in super-resolution reconstruction. Based on the analysis of the existing regularization super-resolution reconstruction algorithm, an adaptive kernel regression filtering core based on Bilateral Total Variation（BTV） is constructed, and it is used as a cost function of regularization super-resolution reconstruction. It can produce kernel function adaptive locally to image features. Compared with traditional regularized reconstruction method, the experimental results show that the algorithm can effectively remove noise, but also can be very good to retain the image details, and has robustness. The objective and subjective evaluation shows that the quality of reconstructed image has significantly improved.