Abstract: In order to enhance the reconstruction ability of regularized super-resolution technique for noisy image, an adaptive threshold denoising method was proposed based on the extended research of General Total Variation (GTV) regularized super-resolution reconstruction. Firstly, the iterative reconstruction was completed according to GTV regularized super-resolution reconstruction. Then, the deduced adaptive threshold matrix was used to divide GTV cost matrix of each iteration procedure by the threshold. The corresponding pixel points whose costs were less than the threshold continued to be iterated while the points whose costs were greater than the threshold were cut down for re-interpolating and canceled from the iteration of this turn. Finally, the reconstruction result was output when the program met the convergence requirement. The experimental results show that, compared with the single GTV regularized reconstruction method and adaptive parameter method, the proposed adaptive threshold denoising method accelerates the convergence rate and improves the quality of reconstruction image, which makes the regularized super-resolution reconstruction technology perform better for noisy image.

Key words: super-resolution reconstruction, regularization technique, General Total Variation (GTV), adaptive threshold, image denoising

摘要： 为了提高正则化超分辨率技术在噪声环境下的重建能力，对广义总变分（GTV）正则超分辨率重建进行了扩展研究，提出了一种自适应阈值去噪的方法。首先，根据GTV正则超分辨率重建算法进行迭代重建；然后，利用推导出的自适应阈值矩阵，对每次迭代产生的代价矩阵进行阈值划分，小于阈值的对应像素点继续迭代，大于阈值的对应像素点被截断后重新插值并不再参与本轮迭代；最后，程序达到收敛条件时输出重建结果。实验结果表明，通过与单一GTV正则重建和自适应参数的方法相比，自适应阈值去噪的方法提高了收敛速度和重建图像的质量，使正则化超分辨率技术在噪声环境下有更好的重建能力。

关键词: 超分辨率重建, 正则化技术, 广义总变分, 自适应阈值, 图像去噪