Until now, neighbor-embedding-based (NE) algorithms for super-resolution (SR) have carried out two independent processes to synthesize high-resolution (HR) image patches. First, neighbor search is performed using the Euclidean distance metric; then, the optimal weights are determined by solving a constrained least squares problem. However, the separate processes are not optimal. A sparse neighbor selection scheme for SR reconstruction is proposed. A larger number of neighbors is first predetermined as potential candidates and develop an extended Robust-SLO algorithm to simultaneously find the neighbors and to solve the reconstruction weights. Recognizing that the k -nearest neighbor ( k -NN) for reconstruction should have similar local geometric structures based on clustering, a local statistical feature, namely histograms of oriented gradients ( HoG) of low-resolution ( LR) image patches is emplied, to perform such clustering. By conveying local structural information of HoG in the synthesis stage, the k -NN of each LR input patch is adaptively chosen from their associated subset, which significantly improves the speed of synthesizing the HR image while preserving the quality of reconstruction. Experimental results suggest that the proposed method can achieve competitive SR quality compared with other state-of-the-art baselines.%到现在为止,基于邻域嵌入法(NE)的图像超分辨(SR)技术都采用两个独立的步骤合成高分辨的图像.首先以Euclidean距离作为标准进行邻域搜索,然后通过求解一个约束最小均方问题得到最优的加权值.然而,采用两个独立的过程并不是最优的.提出一种基于稀疏邻域选择的图像超分辨算法.首先确定可能的邻域范围,然后采用稳健SLO算法同时找出邻域和加权值.由于采用聚类方法,用于重建的k个最近邻域(k-NN)具有相似的局部几何结构,可以采用一种叫做方向梯度直方图(HoG)的统计方法对低分辨图像块进行聚类.通过在合成过程中利用HoG的局部结构信息,每幅低分辨图像的k-NN都能从相对应的子集中自适应的选择,从而在保证合成图像质量的前提下大大提高了合成高分辨图像的速度.仿真表明本文算法能够得到与传统方法相似的结果.
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