基于多字典和稀疏噪声编码的图像超分辨率重建

Super-resolution image reconstruction based on multi-dictionary and sparse-noise coding

稀疏表示模型是通过将字典中的原子进行组合得到期望的结果.为了解决传统字典学习中所有图像块重建均使用同一个字典,从而忽略了最佳稀疏域的问题,提出来一种基于多字典和稀疏噪声编码的图像超分辨率重建算法.在字典训练时,利用图像的特征将它们合理地划分成若干个簇,每个聚类训练生成子字典对,利用最佳字典对进行重建.在求解稀疏系数阶段,引入稀疏编码噪声去除噪声的影响,利用图像非局部自相似性来获得原始图像稀疏编码系数的良好估计,然后将观测图像的稀疏编码系数集中到这些估计当中.实验表明,与ASDS算法和SSIM算法相比较,该算法有更好的重建结果,获得了更丰富的图像细节和更清晰的边缘.

The sparse representation model can obtain the desired result by combining the atoms in the dictionary. In order to solve the problem that all image blocks are reconstructed by using the same dictionary in the traditional dictionary learning, and the optimal sparse domain is ignored, an image reconstruction algorithm based on multi-dictionary and sparse-noise coding is proposed. The image blocks are reasonably divided into several clusters according to the features of the image in dictionary training. Each cluster is trained to generate sub-dictionary pairs, and the best dictionary pairs are used for reconstruction. In the stage of solving the sparse coefficient, the effect of noise is removed by introducing sparse coding noise, a good estimation of the sparse-coding coefficient of the original image is obtained by using the non-local self similarity of the image, and then the sparse-coding coefficients of the observed image are concentrated into these estimates. Experiments show that compared with ASDS algorithm and SSM algorithm, this algorithm has better reconstruction results, and gets richer image details and clearer edges.