Image super-resolution using sparse coding over redundant dictionary based on effective image representations

被引:26
作者
Sajjad, Muhammad [1 ]
Mehmood, Irfan [1 ]
Baik, Sung Wook [1 ]
机构
[1] Sejong Univ, Coll Elect & Informat Engn, Seoul, South Korea
关键词
Super-resolution; Reconstruction; Denoising; Representation-coefficients; Sparse-coding; vercomplete-dictionary; Visual-saliency; Pursuit algorithm; SIMPLE CELLS; REGRESSION;
D O I
10.1016/j.jvcir.2014.10.012
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recent years have shown a growing research interest in the sparse-representation of signals. Signals are described through sparse linear combinations of signal-atoms over a redundant-dictionary. Therefore, we propose a novel super-resolution framework using an overcomplete-dictionary based on effective imagerepresentations such as edges, contours and high-order structures. This scheme recovers the vector of common sparse-representations between low-resolution and corresponding high-resolution imagepatches by solving the l(1)-regularized least-squared problem; subsequently, it reconstructs the HR output by multiplying it with the learned dictionary. The dictionary used in the proposed-technique contains more effective image-representations than those in previous approaches because it contains featuredescriptors such as edges, contours and motion-selective features. Therefore, the proposed-technique is more robust to various types of distortion. A saliency-map quickens this technique by confining the optimization-process to visually salient regions. Experimental analyses confirm the effectiveness of the proposed-scheme, and its quantitative and qualitative performance as compared with other state-of-the-art super-resolution algorithms. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:50 / 65
页数:16
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