基于图像块分类的图像超分辨率重建

针对当前图像超分辨率重建算法中存在的字典单一而导致重建图像质量不佳的问题,提出一种将图像块分类与图像卡通纹理分解相结合的单幅图像超分辨率重建算法。首先,将图像分块,并将图像块分为边缘类、纹理类和平滑类三类,其中纹理类用形态成分分析(MCA)算法分解为卡通部分和纹理部分;然后,对边缘类、卡通部分和纹理部分分别训练高低分辨率字典;最后,求解稀疏系数并与高分辨率字典重建图像块。仿真结果显示,与基于稀疏表示的超分辨率重建(SCSR)算法和单幅图像超分辨率重建(SISR)算法相比,所提算法的峰值信噪比(PNSR)值分别提高了0. 26 d B和0. 14 d B,表明该算法的重建效果更好,重建图像纹理细节更丰富。

Image super-resolution reconstruction based on image patch classification

Concerning the poor quality of existing image super-resolution reconstruction caused by single dictionary, a new single image super-resolution algorithm based on classified image patches and image cartoon-texture decomposition was proposed. Firstly, an image was divided into image patches which were classified into smooth patches, edge patches and texture patches, and the texture class was divided into cartoon part and texture part by Morphological Component Analysis (MCA) algorithm. Secondly, ege patches, cartoon part and texture part of texture patches were applied respectively to train the dictionaries of low-resolution and high-resolution. Finally, the sparse coefficients were calculated, then the image patches were reconstructed by using the corresponding high-resolution dictionary and sparse coefficients. In the comparison experiments with Sparse Coding Super-Resolution (SCSR) algorithm and Single Image Super-Resolution (SISR) algorithm, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm was increased by 0.26 dB and 0.14 dB respectively. The experimental results show that the proposed algorithm can obtain more details in texture with better reconstruction effect.