CAJ | 학술논문

提出一种基于局部几何结构相似性和协同表示的超分辨率图像重建算法。该算法利用l2范数正则化的协同表示和局部几何相似约束模型求解低分辨率图像块在低分辨率字典下的线性表示系数，并利用这一系数重构出高分辨率图像块。文中基于l2范数的系数求解模型可得到解析解而不涉及局部最小解，相较于l1稀疏性约束具有较低的复杂度。实验结果表明，该算法对小尺寸超分辨率图像重建可行且有效，并在重构效果上具有明显的优越性。进一步研究表明，在放大因子增大和存在噪声的情况下，该算法较传统算法重构效果也有显著提高。
제출일충기우국부궤하결구상사성화협동표시적초분변솔도상중건산법。해산법이용l2범수정칙화적협동표시화국부궤하상사약속모형구해저분변솔도상괴재저분변솔자전하적선성표시계수，병이용저일계수중구출고분변솔도상괴。문중기우l2범수적계수구해모형가득도해석해이불섭급국부최소해，상교우l1희소성약속구유교저적복잡도。실험결과표명，해산법대소척촌초분변솔도상중건가행차유효，병재중구효과상구유명현적우월성。진일보연구표명，재방대인자증대화존재조성적정황하，해산법교전통산법중구효과야유현저제고。
An approach for super-resolution image reconstruction is presented based on local structural similarity and collaborative representation. The collaborative representation l2-norm regularization and local similarity constraint are employed to seek a linear combination for a patch of low-resolution input image with respect to the low-resolution dictionary. Then, the high-resolution image patch is generated by virtue of the coefficients of this combination and the high-resolution dictionary. In addition, the l2-norm based objective function implies an analytical solution and it does not involve local minima. Hence, it performs at a lower complexity compared to l1-sparsity constraint model. The experimental results demonstrate that the proposed method is feasible and effective for small super-resolution image reconstruction and outperforms the bicubic interpolation method and sparse representation super-resolution model on both visual effect and numerical results. Further research shows that the proposed method also performs well for large magnification factors and noisy data.