A NEW IMAGE RESOLUTION TECHNIQUE USING NEIGHBOR EMBEDDING

algorithms for super resolution (SR) have carried out two independent processes to synthesize high resolution (HR) image patches. In our paper we propose a sparse Neighbor selection scheme for SR reconstruction We first predetermine a larger number of neighbors as potential candidates and develop an extended Robust-SL0 algorithm to simultaneously find the neighbors and to solve the reconstruction weights. Recognizing that the nearest neighbor (k-NN) for reconstruction should have similar local geometric structures based on clustering, we employ a local statistical feature, namely histograms of oriented gradients (HoG) of low-resolution (LR) image patches, to perform such clustering. By conveying local structural information of HoG in the synthesis stage, the-NN of each LRinput 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 competitiv