探索稀疏恢复对图像超分辨率质量的影响
Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution
AI总结：
本文研究稀疏恢复算法选择对基于耦合字典的图像超分辨率重建质量的影响，并通过实验寻找最佳稀疏恢复算法。
摘要：
字典学习可用于图像超分辨率，方法是从高分辨率与低分辨率图像对中学习一对耦合的图像块字典，使得对应图像块在由耦合字典表示时共享相同的稀疏向量。随后，这些字典可用于基于稀疏恢复从低分辨率输入图像重建对应的高分辨率图像块。其思想是利用低分辨率字典恢复共享的稀疏向量，再将其乘以高分辨率字典以恢复对应的高分辨率图像块。在本工作中，我们研究所使用的稀疏恢复算法对重建图像质量的影响。我们提供经验实验，以寻找可用于此目的的最佳稀疏恢复算法。
英文摘要：
Dictionary learning can be used for image superresolution by learning a pair of coupled dictionaries of image patches from high-resolution and low-resolution image pairs such that the corresponding pairs share the same sparse vector when represented by the coupled dictionaries. These dictionaries then can be used to to reconstruct the corresponding high-resolution patches from low-resolution input images based on sparse recovery. The idea is to recover the shared sparse vector using the low-resolution dictionary and then multiply it by the high-resolution dictionary to recover the corresponding high-resolution image patch. In this work, we study the effect of the sparse recovery algorithm that we use on the quality of the reconstructed images. We offer empirical experiments to search for the best sparse recovery algorithm that can be used for this purpose.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
