基于即插即用框架的脉冲噪声图像复原算法  

Image restoration algorithm based on plug-and-play regularization to remove impulse noise

作　　者：梅金金 MEI Jinjin(School of Mathematics and Statistic,Fuyang Normal University,Fuyang Anhui 236037,China)

机构地区：[1]阜阳师范大学数学与统计学院,安徽阜阳236037

出　　处：《阜阳师范大学学报（自然科学版）》2023年第4期1-7,共7页Journal of Fuyang Normal University:Natural Science

基　　金：国家自然科学基金项目(11901101);安徽自然科学基金项目(1908085QA08);安徽省高校自然科学研究重点项目(2023AH050406,KJ2020A0535,KJ2021A1253,KJ2021A0658,KJ20210A0682);安徽省高校杰出青年科研项目(2022AH020082);阜阳师范大学青年人才重点项目(rcxm202103);阜阳师范大学自然科学一般项目(2020FSKJ11,2021FSKJ13)资助。

摘　　要：针对脉冲噪声下的图像复原存在全变分正则化方法会在图像的光滑区域产生阶梯效应、L1保真项不能很好地刻画真实图像与退化图像的关系等问题,本文结合卷积神经网络和Lp保真项,提出一种即插即用的数学优化模型,利用交替方向迭代算法求解所提出的模型。数值实验结果表明,与L1保真项相比,所提出模型获得的复原图像能够有效地去除脉冲噪声,并在边缘、纹理和光滑度等方面有较好的应用效果。For the staircase effect in smooth regions of the image and the inability of L1 fidelity terms to accurately depict the relationship between real and degraded images,this paper proposes a plug and play mathematical optimization model by using alternating direction iteration algorithm that combines convolutional neural networks and Lp fidelity terms to address the problems of total variational regularization methods in image restoration under pulse noise.The numerical experimental results show that compared with the L1 fidelity term,the restored images obtained by the proposed model can effectively remove pulse noise and maintain good edge,texture,smoothness and other aspects.

关 键 词：图像复原 Lp保真项 卷积神经网络 即插即用 交替方向迭代算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]