Sparse Representation Based Image Super-resolution Using Large Patches
Sparse Representation Based Image Super-resolution Using Large Patches
复制标题
使用大补丁的基于稀疏表示的图像超分辨率
DOI：
10.1049/cje.2018.05.011
复制
发表时间：
2018
影响因子：
1.2
通讯作者：
Ke Dengfeng
中科院分区：
文献类型：
--
作者：
Liu Ning;Zhou Pan;Liu Wenju;Ke Dengfeng
本文解决了从低分辨率图像生成高分辨率图像的问题。基于字典的超分辨方法已经被提出并在超分辨应用中取得了巨大的成功。这些方法大多使用小块作为字典原子，并利用统一的字典对对每个块进行重建，这可能会限制超分辨率性能。我们使用大的补丁，而不是小的联合收割机的字典和进行补丁重建。由于大的补丁包含更多的有意义的信息比一个小的，重建结果可能会有更多的高频细节。为了保证大补丁字典的完整性，字典的规模也应该很大。为了处理大型字典的存储和计算问题，我们采用了二进制编码方法。该方法能较好地保持面片的局部信息。对于低分辨率图像中的每个块，我们在低分辨率字典中搜索其相似块以获得子字典。我们计算其稀疏表示以获得相应的高分辨率版本。全局重构约束的强制执行，以消除SR结果和地面真理之间的差异。实验结果表明，该方法优于其他超分辨率方法，特别是当放大倍数较大或图像被白色高斯噪声模糊时。
This paper addresses the problem of generating a high‐resolution image from a low‐resolution image. Many dictionary based methods have been proposed and have achieved great success in super resolution application. Most of these methods use small patches as dictionary atoms, and utilize a unified dictionary pair to conduct reconstruction for each patch, which may limit the super resolution performance. We use large patches instead of small ones to combine a dictionary and to conduct patch reconstruction. Since a large patch contains more meaningful information than a small one, the reconstruction result may have more high frequency details. To guarantee the completeness of the dictionary with large patch, the scale of the dictionary should be large as well. To handle the storage and calculation problems with large dictionaries, we adopt a binary encoding method. This method can preserve local information of patches. For each patch in the low‐resolution image, we search its similar patches in the low‐resolution dictionary to obtain a sub‐dictionary. We compute its sparse representation to get the corresponding high‐resolution version. Global reconstruction constraint is enforced to eliminate the discrepancy between the SR result and the ground truth. Experimental results demonstrate that our method outperforms other super resolution methods, especially when the magnification factor is large or the image is blurred by white Gaussian noise.