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.

中文翻译：

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WDN：一个宽而深的网络来分而治之的图像超分辨率

分而治之是一种成熟的算法设计范式，已经证明它可以有效地解决各种问题。然而，用神经网络解决问题，尤其是图像超分辨率问题，还有待充分探索。在这项工作中，我们提出了一种将图像超分辨率问题划分为多个子问题，然后在神经网络的帮助下解决/克服它们的方法。与典型的深度神经网络不同，我们设计了一种比现有网络更广泛（同时更深）的替代网络架构，并且专门设计用于使用神经网络实现分而治之的设计范式。此外，还引入了一种校准特征图像素强度的技术。