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ODD: An Algorithm of Online Directional Dictionary Learning for Sparse Representation

机译：ODD：稀疏表示的在线方向词典学习算法

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摘要

Recently, some sparse representation based image reconstruction methods have demonstrated with a learnt dictionary. In this paper, we propose a block-based image sparse representation approach with an online directional dictionary (ODD). Unlike the conventional dictionary learning approaches for image sparse representation aims at learning some signal patterns from a large set of training image patches, the proposed joint dictionary for each patch is composed by an original offline or online trained sub-dictionary from a training set and an novel adaptive directional sub-dictionary estimated from the reconstructed nearby pixels of the patch itself. A joint dictionary with ODD has two main advantages compared with the conventional dictionaries. First, for each patch to be sparse represented, not only the most general contents, but also the most possible directional textures of the image patch are considered to improve the reconstruction performance. Second, in order to save storage costs, only the original trained sub-dictionary should be stored, the proposed ODD can be obtained consistently. Experimental results show that the reconstruction performance of the proposed approach exceeds other competitive dictionary learning based image sparse representation methods, validating the superiority of our approach.
机译：最近，一些基于稀疏表示的图像重建方法已经通过学习词典进行了演示。在本文中，我们提出了一种带有在线方向字典（ODD）的基于块的图像稀疏表示方法。与用于图像稀疏表示的常规字典学习方法旨在从大量训练图像补丁中学习一些信号模式不同，针对每个补丁提出的联合字典由来自训练集和一种新颖的自适应方向子字典，可从补丁本身附近重构的像素进行估算。与常规词典相比，带有ODD的联合词典具有两个主要优点。首先，对于要稀疏表示的每个补丁，不仅考虑了图像补丁的最一般的内容，而且考虑了最可能的方向纹理，以提高重建性能。其次，为了节省存储成本，仅应存储原始经过训练的子词典，可以始终获得提议的ODD。实验结果表明，该方法的重建性能优于其他基于竞争字典学习的图像稀疏表示方法，验证了该方法的优越性。

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