Abstract
State-of-the-art approaches toward image restoration can be classified into model-based and learning-based. The former - best represented by sparse coding techniques - strive to exploit intrinsic prior knowledge about the unknown high-resolution images; while the latter - popularized by recently developed deep learning techniques - leverage external image prior from some training dataset. It is natural to explore their middle ground and pursue a hybrid image prior capable of achieving the best in both worlds. In this paper, we propose a systematic approach of achieving this goal called Structured Analysis Sparse Coding (SASC). Specifically, a structured sparse prior is learned from extrinsic training data via a deep convolutional neural network (in a similar way to previous learning-based approaches); meantime another structured sparse prior is internally estimated from the input observation image (similar to previous model-based approaches). Two structured sparse priors will then be combined to produce a hybrid prior incorporating the knowledge from both domains. To manage the computational complexity, we have developed a novel framework of implementing hybrid structured sparse coding processes by deep convolutional neural networks. Experimental results show that the proposed hybrid image restoration method performs comparably with and often better than the current state-of-the-art techniques.
Abstract (translated)
最先进的图像恢复方法可分为基于模型和基于学习。前者 - 最好用稀疏编码技术代表 - 努力利用关于未知高分辨率图像的内在先验知识;而后者 - 由最近开发的深度学习技术推广 - 利用一些训练数据集之前的外部图像。探索自己的中间地带并追求一种能够在两个世界中实现最佳效果的混合图像是很自然的。在本文中，我们提出了一种实现这一目标的系统方法，称为结构化分析稀疏编码（SASC）。具体地，通过深度卷积神经网络从外部训练数据中学习结构化稀疏先验（以与先前基于学习的方法类似的方式）;同时，从输入观察图像内部估计另一个结构化稀疏先验（类似于先前基于模型的方法）。然后将两个结构化的稀疏先验组合以产生混合，然后结合来自两个领域的知识。为了管理计算复杂性，我们开发了一种通过深度卷积神经网络实现混合结构化稀疏编码过程的新框架。实验结果表明，所提出的混合图像恢复方法与当前最先进的技术相比并且通常更好。
URL
https://arxiv.org/abs/1807.06920
