IEEE Trans Image Process - Image super-resolution with sparse neighbor embedding.

Tópicos

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Resumo

Until now, neighbor-embedding-based (NE) algorithms for super-resolution (SR) have carried out two independent processes to synthesize high-resolution (HR) image patches. In the first process, neighbor search is performed using the Euclidean distance metric, and in the second process, the optimal weights are determined by solving a constrained least squares problem. However, the separate processes are not optimal. In this 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 k-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 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.

Resumo Limpo

now neighborembeddingbas ne algorithm superresolut sr carri two independ process synthes highresolut hr imag patch first process neighbor search perform use euclidean distanc metric second process optim weight determin solv constrain least squar problem howev separ process optim paper propos spars neighbor select scheme sr reconstruct first predetermin larger number neighbor potenti candid develop extend robustsl algorithm simultan find neighbor solv reconstruct weight recogn knearest neighbor knn reconstruct similar local geometr structur base cluster employ local statist featur name histogram orient gradient hog lowresolut lr imag patch perform cluster convey local structur inform hog synthesi stage knn lr input patch adapt chosen associ subset signific improv speed synthes hr imag preserv qualiti reconstruct experiment result suggest propos method can achiev competit sr qualiti compar stateoftheart baselin

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