用迭代收缩法优化字典学习，提升图像恢复质量
Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage
- 基于收缩操作构建多种稀疏优化方法
- 数据集越大，重建误差越低，效果更稳定
- 适合研究图像恢复与稀疏表示的开发者
本文研究了图像恢复中的稀疏字典学习问题。针对该问题，我们评估并比较了多种基于收缩操作的先进稀疏优化方法。以在线算法为框架，结合凸优化中的基追踪去噪（Basis Pursuit Denoising）问题，通过精心设计的数据集与字典，研究了训练数据库规模扩大对重建质量的影响，采用多种误差度量进行分析。结果表明，在不同训练数据设置下，优化方法的选择对实际性能有显著影响。同时，我们还分析了各方法在不同场景下的计算效率。
原文摘要 · Abstract (English)
In this paper we study the sparse coding problem in the context of sparse dictionary learning for image recovery. To this end, we consider and compare several state-of-the-art sparse optimization methods constructed using the shrinkage operation. As the mathematical setting of these methods, we consider an online approach as algorithmical basis together with the basis pursuit denoising problem that arises by the convex optimization approach to the dictionary learning problem. By a dedicated construction of datasets and corresponding dictionaries, we study the effect of enlarging the underlying learning database on reconstruction quality making use of several error measures. Our study illuminates that the choice of the optimization method may be practically important in the context of availability of training data. In the context of different settings for training data as may be considered part of our study, we illuminate the computational efficiency of the assessed optimization methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。