稀疏字典编码的超分辨率重建

基于学习的超分辨率方法通常根据低分辨率图像从样本库中选取若干特征相似的匹配对象,再使用优化算法进行超分辨率估计,但其结果受匹配对象的质量限制,并且匹配特征一般只选择图像的几何结构信息,匹配准确性较低.提出了稀疏字典编码的超分辨率模型,将高、低分辨率图像特征块统一进行稀疏编码,建立高、低分辨率图像的稀疏关联,同步实现匹配搜索和优化估计,突破了上述方法的限制.应用形态分量分析法提取图像的特征数据,提高了特征匹配的准确性,并同步实现超分辨率重建和降噪功能.优化方法采用稀疏K-SVD算法以提高稀疏字典编码的计算速度.采用自然图像进行实验与其他基于学习的超分辨率算法相比,重建所得到的图像质量更优.

Super-Resolution Based on Sparse Dictionary Coding

Learning-Based super-resolution methods usually select several objects with similar features from some examples according to the low-resolution image,then estimate super-resolution result using optimization algorithm.But the result is usually limited by the quality of matching objects and only geometric construction of the images is selected as matching feature,so matching accuracy is relatively low.This paper presents a sparse dictionary model for image super-resolution,which unifies the feature patches of high-resolution(HR) and low-resolution(LR) images for sparse coding.To break through the aforementioned limitations,this method builds a sparse association between HR and LR images,and realized simultaneous matching and optimization methods.The study uses a MCA method to improve the accuracy for feature extraction and carry out super-resolution reconstruction and denoise simultaneously.Sparse K-SVD algorithm is adopted as optimization method to reduce the computation time of sparse coding.Some experiments with real images show that this method outperforms other learning-based super-resolution algorithms.