In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation...In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.展开更多
目的像对稠密匹配是视觉定位、影像融合、超分辨率重建等高级图像处理技术的基础,由于像对可能受多种摄影条件的影响,导致难以获得高效的稠密匹配结果,为此提出一种结合密度聚类平滑约束与三角网等比例剖分的像对稠密匹配方法。方法为...目的像对稠密匹配是视觉定位、影像融合、超分辨率重建等高级图像处理技术的基础,由于像对可能受多种摄影条件的影响,导致难以获得高效的稠密匹配结果,为此提出一种结合密度聚类平滑约束与三角网等比例剖分的像对稠密匹配方法。方法为了快速获得同名点集,采用ORB(oriented FAST and rotated BRIEF)算法获取稀疏匹配点集,利用积分图筛选出以该特征点为中心的邻域中密度直达的特征点数目,计算像对间每个特征点对的偏移角、位置信息以及欧氏距离后进行密度估计聚类,通过平滑约束条件扩充聚类中的特征点对,从而快速获得内点集。证明了三角剖分在仿射变换下的等比例性质,以内点集为基础构建三角网,利用该性质分别计算像对中对应三角网内部等比例点的位置,并利用这些等比例点校验两个三角区域的相似性,进一步提纯内点集。最后,利用提纯后的内点集计算稠密匹配点位置,作为最后的稠密匹配结果。结果在多个具有尺度缩放、重复纹理、旋转的公共数据集上进行像对匹配实验,实验结果表明,本文方法具备一定的抗旋转、尺度变化与重复纹理能力,能够较好地避免由于某些局部外点造成仿射变换矩阵估计不准确而影响整体平面稠密匹配准确率的情况,同时保证快速获得足够稠密的匹配结果。结论实验结果验证了本文方法的有效性与实用性,其结果可应用于后期高级图像处理技术中。展开更多
基金The National Natural Science Foundation of China(No.61374194,No.61403081)the National Key Science&Technology Pillar Program of China(No.2014BAG01B03)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20140638)Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.
文摘目的像对稠密匹配是视觉定位、影像融合、超分辨率重建等高级图像处理技术的基础,由于像对可能受多种摄影条件的影响,导致难以获得高效的稠密匹配结果,为此提出一种结合密度聚类平滑约束与三角网等比例剖分的像对稠密匹配方法。方法为了快速获得同名点集,采用ORB(oriented FAST and rotated BRIEF)算法获取稀疏匹配点集,利用积分图筛选出以该特征点为中心的邻域中密度直达的特征点数目,计算像对间每个特征点对的偏移角、位置信息以及欧氏距离后进行密度估计聚类,通过平滑约束条件扩充聚类中的特征点对,从而快速获得内点集。证明了三角剖分在仿射变换下的等比例性质,以内点集为基础构建三角网,利用该性质分别计算像对中对应三角网内部等比例点的位置,并利用这些等比例点校验两个三角区域的相似性,进一步提纯内点集。最后,利用提纯后的内点集计算稠密匹配点位置,作为最后的稠密匹配结果。结果在多个具有尺度缩放、重复纹理、旋转的公共数据集上进行像对匹配实验,实验结果表明,本文方法具备一定的抗旋转、尺度变化与重复纹理能力,能够较好地避免由于某些局部外点造成仿射变换矩阵估计不准确而影响整体平面稠密匹配准确率的情况,同时保证快速获得足够稠密的匹配结果。结论实验结果验证了本文方法的有效性与实用性,其结果可应用于后期高级图像处理技术中。
