Abstract
Many real-world solutions for image restoration are learning-free and based on handcrafted image priors such as self-similarity. Recently, deep-learning methods that use training data have achieved state-of-the-art results in various image restoration tasks (e.g., super-resolution and inpainting). Ulyanov et al. bridge the gap between these two families of methods (CVPR 18). They have shown that learning-free methods perform close to the state-of-the-art learning-based methods (approximately 1 PSNR). Their approach benefits from the encoder-decoder network. In this paper, we propose a framework based on the multi-level extensions of the encoder-decoder network, to investigate interesting aspects of the relationship between image restoration and network construction independent of learning. Our framework allows various network structures by modifying the following network components: skip links, cascading of the network input into intermediate layers, a composition of the encoder-decoder subnetworks, and network depth. These handcrafted network structures illustrate how the construction of untrained networks influence the following image restoration tasks: denoising, super-resolution, and inpainting. We also demonstrate image reconstruction using flash and no-flash image pairs. We provide performance comparisons with the state-of-the-art methods for all the restoration tasks above.
Abstract (translated)
许多图像恢复的现实解决方案都是免费学习的，并且基于手工制作的图像先验，例如自相似性。最近，使用训练数据的深度学习方法在各种图像恢复任务（如超分辨率和修复）中取得了最先进的效果。Ulyanov等人弥合这两个方法系列（CVPR 18）之间的差距。他们已经表明，学习自由方法的表现接近于最先进的基于学习的方法（大约1 psnr）。他们的方法得益于编码器-解码器网络。在本文中，我们提出了一个基于编码器-解码器网络多级扩展的框架，以研究图像恢复和独立于学习的网络结构之间关系的有趣方面。我们的框架允许通过修改以下网络组件来实现各种网络结构：跳过链接、将网络输入级联到中间层、编码器-解码器子网络的组成以及网络深度。这些手工制作的网络结构说明了未经训练的网络结构如何影响以下图像恢复任务：去噪、超分辨率和修复。我们还演示了使用flash和no flash图像对的图像重建。我们为上述所有修复任务提供与最先进方法的性能比较。
URL
https://arxiv.org/abs/1905.00322
