Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (7): 2084-2088.DOI: 10.11772/j.issn.1001-9081.2017.07.2084

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Adaptive threshold denoising of regularized super-resolution reconstruction procedure

PENG Zheng1,2, CHEN Dongfang1,2, WANG Xiaofeng1,2   

  1. 1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;
    2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System(Wuhan University of Science and Technology), Wuhan Hubei 430065, China
  • Received:2016-12-08 Revised:2017-01-26 Online:2017-07-10 Published:2017-07-18
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61273225, 61572381).


彭政1,2, 陈东方1,2, 王晓峰1,2   

  1. 1. 武汉科技大学 计算机科学与技术学院, 武汉 430065;
    2. 智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学), 武汉 430065
  • 通讯作者: 彭政
  • 作者简介:彭政(1993-),男,湖南长沙人,硕士研究生,主要研究方向:超分辨率重建;陈东方(1967-),男,湖北天门人,教授,博士,CCF会员,主要研究方向:图像处理、机器学习;王晓峰(1982-),男,河南郑州人,副教授,博士,主要研究方向:超分辨率重建、机器学习。
  • 基金资助:

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

CLC Number: