论文标题
WDN：一个宽阔而深的网络，用于划分图像超分辨率
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution
论文作者
论文摘要
Divide and Conquer是一种已建立的算法设计范式，已证明自己有效地解决了各种问题。但是，在解决神经网络的问题（尤其是图像超分辨率的问题）中，尚未充分探索它。在这项工作中，我们提出了一种将图像超分辨率问题分为多个子问题的方法，然后在神经网络的帮助下解决/征服它们。与典型的深神经网络不同，我们设计了一种替代网络体系结构，它比现有网络更宽（加上更深的网络），并且是专门设计的，旨在通过神经网络实现分界线和争议设计范式。此外，正在引入一种校准特征图像素的强度的技术。在五个数据集上进行了广泛的实验表明，我们对问题和所提出的体系结构的方法比当前最新方法产生更好，更清晰的结果。
Divide and conquer is an established algorithm design paradigm that has proven itself to solve a variety of problems efficiently. However, it is yet to be fully explored in solving problems with a neural network, particularly the problem of image super-resolution. In this work, we propose an approach to divide the problem of image super-resolution into multiple sub-problems and then solve/conquer them with the help of a neural network. Unlike a typical deep neural network, we design an alternate network architecture that is much wider (along with being deeper) than existing networks and is specially designed to implement the divide-and-conquer design paradigm with a neural network. Additionally, a technique to calibrate the intensities of feature map pixels is being introduced. Extensive experimentation on five datasets reveals that our approach towards the problem and the proposed architecture generate better and sharper results than current state-of-the-art methods.
