摘要
针对单幅含噪图像的超分辨率重建问题,基于图像在过完备字典下的稀疏表示建立了超分辨率重建模型.该模型中低分辨率字典采用K-SVD算法直接训练,高分辨率字典则由高分辨率图像块与低分辨率字典下的同构的表示系数进行逼近求得;近似的高分辨率图像块通过高分辨率字典乘以表示系数得到,为使重建结果对噪声具有鲁棒性,利用基于稀疏表示的噪声图像恢复的方法由重叠的近似高分辨率图像块求得最终结果.实验结果表明,文中模型无论是主观视觉还是客观评价指标均取得了较好的效果,并验证了模型及算法的有效性.
Aiming at the problem of super-resolution reconstruction for single noised image, in terms of sparse representation of over-complete dictionary, a super-resolution model is proposed. The K-SVD algorithm is used directly for learning the dictionary for low-resolution images. The dictionary for high-resolution images is got by optimizing the approximating error of the isomorphic sparse representation coefficients, which are got by learning the dictionary for low-resolution images. The representation coefficients are multiplied by the high-resolution dictionary to get the approximative high-resolution image patches. To make the reconstructed image robust to noise, the denoising method via sparse representation is used to get the final image from the overlapped approximative high- resolution image patches. The experimental results show that the proposed model obtains better outcome both in subjective visual effect and objective evaluation criteria, and demonstrates the effective of the model and algorithm.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2012年第12期1599-1605,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
安徽省自然科学基金(1208085QF115)
关键词
超分辨率
单幅图像
稀疏表示
过完备字典
K—SVD
super-resolution
single image
sparse representation
over-complete dictionary
K-SVD