基于图像块迭代和稀疏表示的超分辨率图像重建算法

Super-resolution image reconstruction algorithm based on image patche iteration and sparse representation

针对待复原图像内容间差异和重建速度缓慢的问题,提出基于图像块迭代分类和稀疏表示的超分辨率图像重建算法。首先,根据阈值把图像迭代分块为三种不同形态。然后,对三种形态分别处理:在重建时,对4N×4N块利用双三次插值(BI)算法重建;对2N×2N块由K-奇异值分解(K-SVD)算法得到对应的高、低分辨率字典,通过正交匹配追踪(OMP)算法重建;对N×N块用形态成分分析(MCA)法分解为平滑层和纹理层,然后由各层相应的字典对通过OMP算法重建。将所提方法与基于稀疏基的方法、基于MCA的方法和基于两级与分频带字典的方法相比,所提算法在主观视觉效果、评测指标和重建速度上都有明显的改善。实验结果表明,该方法在图像的边缘块和不规则区域获得了更为精细的细节,重建效果更明显。

Concerning the slow reconstruction and the difference among the contents of the image to be reconstructed, an improved super-resolution image reconstruction algorithm based on image patche iteration and sparse representation was proposed. In the proposed method, image patches were firstly divided into three different forms by threshold features, then the three forms were treated separately： during the reconstruction process, Bicubic Interpolation（ BI） approach was used for image patches of 4N × 4N; image patches of 2N × 2N achieved corresponding high and low resolution dictionary pairs by K-Singular Value Decomposition（ K-SVD） algorithm, and then to finish reconstruction using Orthogonal Matching Pursuit（ OMP）algorithm; image patches of N × N were divided into smoothing layer and texture layer by Morphological Component Analysis（ MCA） algorithm, then to finish reconstruction using OMP with corresponding dictionary pairs of each layer. Compared with the methods based on sparse representation group, MCA, and two-stage multi-frequency-band dictionaries, the proposed algorithm has a significant improvement in subjective visual effect, evaluation index and reconstruction speed. The experimental results show that the proposed algorithm can obtain more details in edge patches and irregular structure regions with better reconstruction effect.