论文标题
学习深层分析词典 - 第二部分：卷积词典
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries
论文作者
论文摘要
在本文中，我们通过学习卷积词典而不是非结构化词典来介绍深度卷积分析词典模型（DEEPCAM），如伴侣论文中引入的深度分析词典模型。卷积词典更适合处理高维信号，例如图像，只有少量的自由参数。通过利用卷积词典的属性，我们提出了一种有效的卷积分析词典学习方法。 L层的DeepCAM由L卷卷层词典和元素软阈值对以及单层卷积合成词典组成。与DEEPAM相似，每个卷积分析字典都由卷积信息保存分析词典（iPad）和卷积聚类分析词典（CAD）组成。使用拟议的学习算法的变体学习iPad和CAD。我们证明了DeepCam是一个有效的多层卷积模型，并且在单像超分辨率上，可以达到与其他方法相当的性能，同时还显示出良好的概括能力。
In this paper, we introduce a Deep Convolutional Analysis Dictionary Model (DeepCAM) by learning convolutional dictionaries instead of unstructured dictionaries as in the case of deep analysis dictionary model introduced in the companion paper. Convolutional dictionaries are more suitable for processing high-dimensional signals like for example images and have only a small number of free parameters. By exploiting the properties of a convolutional dictionary, we present an efficient convolutional analysis dictionary learning approach. A L-layer DeepCAM consists of L layers of convolutional analysis dictionary and element-wise soft-thresholding pairs and a single layer of convolutional synthesis dictionary. Similar to DeepAM, each convolutional analysis dictionary is composed of a convolutional Information Preserving Analysis Dictionary (IPAD) and a convolutional Clustering Analysis Dictionary (CAD). The IPAD and the CAD are learned using variations of the proposed learning algorithm. We demonstrate that DeepCAM is an effective multilayer convolutional model and, on single image super-resolution, achieves performance comparable with other methods while also showing good generalization capabilities.
