基于稀疏编码的鲁棒型人脸超分辨率重建  

Robust Face Super-resolution Reconstruction Based on Sparse Coding

作　　者：刘芳华[1] 阮若林[2] 倪浩[1] 王建峰[3] 

机构地区：[1]湖北科技学院电子与信息工程学院,湖北咸宁437100 [2]湖北科技学院生物医学工程学院,湖北咸宁437100 [3]湖北科技学院网络管理中心,湖北咸宁437100

出　　处：《电讯技术》2017年第8期957-962,共6页Telecommunication Engineering

基　　金：国家自然科学基金资助项目(61271256);湖北省教育厅科研计划指导性项目(B2017183);湖北省自然科学基金资助项目(2015CFB452);湖北省高等学校优秀中青年科技创新团队计划项目(T201513)

摘　　要：为了减少人脸超分图像的边缘伪影和图像噪点,利用基于稀疏编码的单幅图像超分辨率重建算法,在字典学习阶段,结合L1范数引入在线字典学习方法,使字典根据当前输入图像块和上次迭代生成的字典逐列更新,得到更加精确的超完备字典对,用于图像重建。实验中进行的仿真结果表明,改进算法超分结果的峰值信噪比(PSNR)和结构相似性(SSIM)比同类型的稀疏编码超分法(SCSR)和应用在线字典学习算法的超分方法(ODLSR)均有较大幅度提升,比后者平均提升0.72 d B和0.018 7。同时,视觉上有效地消除了边缘伪影,且在处理含噪人脸图像时,具备更强的去噪能力和更好的鲁棒性。In order to reduce the artifacts and noises accompanied with the edges of face super-resolution im-ages, the improved algorithm uses the super-resolution model based on sparse coding. In the dictionary learning phase,L1-norm is combined into online dictionary learning which is used as the dictionary training method. The generated dictionary is updated column by column according to the present input image patches and the previous iterated dictionary. Thus the more accurate overcomplete dictionaries can be acquired to re-construct the final image. Comparisons of simulation results in the experiment show that the peak signal-to-noise ratio（PSNR） and structural similarity（SSIM） of the proposed method are much bigger than those of sparse coding super-resolution algorithm（SCSR） and online dictionary learning super-resolution algorithm （ODLSR）. The average promotion quantity to the latter algorithms is 0. 72 dB and 0. 0187,respectively. The artifacts along the edges are eliminated effectively. The denoising capability and robustness of the pro-posed algorithm are much better than those of both SCSR and ODLSR in processing noisy face images.

关 键 词：人脸图像 超分辨率重建 稀疏编码 在线字典学习 

分 类 号：TN919.8[电子电信—通信与信息系统]