论文标题
卷积非线性词典带有级联结构滤波器
Convolutional Nonlinear Dictionary with Cascaded Structure Filter Banks
论文作者
论文摘要
这项研究提出了使用级联过滤器库进行图像恢复的卷积非线性词典（CNLD）。通常，卷积神经网络（CNN）证明了它们在图像恢复应用中的实用性。但是，现有的CNN是构建的，而无需考虑原子图像之间的关系（卷积内核）。结果，还有讨论设计空间的作用的空间。为了提供一个构建有效且结构化的卷积网络的框架，本研究提出了CNLD。反向传播学习过程来自某些图像恢复实验，从而验证了CNLD的重要性。证明参数的数量在保留恢复性能的同时减少了。
This study proposes a convolutional nonlinear dictionary (CNLD) for image restoration using cascaded filter banks. Generally, convolutional neural networks (CNN) demonstrate their practicality in image restoration applications; however, existing CNNs are constructed without considering the relationship among atomic images (convolution kernels). As a result, there remains room for discussing the role of design spaces. To provide a framework for constructing an effective and structured convolutional network, this study proposes the CNLD. The backpropagation learning procedure is derived from certain image restoration experiments, and thereby the significance of CNLD is verified. It is demonstrated that the number of parameters is reduced while preserving the restoration performance.
