核偏最小二乘算法的图像超分辨率算法  

Image Super-Resolution Using KPLS

作　　者：吴炜[1] 杨晓敏[1] 余艳梅[1] 石一兴[1] 何小海[1] 

机构地区：[1]四川大学电子信息学院,成都610064

出　　处：《电子科技大学学报》2011年第1期105-110,共6页Journal of University of Electronic Science and Technology of China

基　　金：教育部重点项目(107094);国家自然科学基金(61071161)

摘　　要：提出了基于核偏最小二乘算法(KPLS)回归的超分辨率复原算法。该算法首先将高低分辨率图像块的高频信息和中频信息作为建立回归关系的特征,并对图像进行分块;依据相应的高低分辨率图像块的关系,使用KPLS建立起回归模型;在复原时,依据该模型回归得到高分辨率的图像块,将图像块拼接为高分辨率的图像。通过对人脸图像和车牌图像的实验结果,表明该算法无论是对人脸图像还是车牌图像都能取得较好的复原效果。A learning-based super-resolution algorithm based on Kernel Partial Least Squares（KPLS） regression is proposed.First,KPLS regression algorithm is introduced.Then a super-resolution algorithm based on KPLS regression is analyzed.High resolution images use the high-frequency information as their feature,while low resolution images use middle-frequency as their features.Based on the relationship of the high and low resolution images,KPLS is used to set up regression model.The regression model is applied to infer high-resolution image.The experimental results show that our method can achieve very good results to face images and car plate images.The results of our method are closer to the real images.

关 键 词：图像复原 核偏最小二乘法(KPLS) 基于学习的超分辨率 回归算法 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]