Scetbon Meyer, Elad Michael, Milanfar Peyman
IEEE Trans Image Process. 2021;30:5944-5955. doi: 10.1109/TIP.2021.3090531. Epub 2021 Jun 29.
This work considers noise removal from images, focusing on the well-known K-SVD denoising algorithm. This sparsity-based method was proposed in 2006, and for a short while it was considered as state-of-the-art. However, over the years it has been surpassed by other methods, including the recent deep-learning-based newcomers. The question we address in this paper is whether K-SVD was brought to its peak in its original conception, or whether it can be made competitive again. The approach we take in answering this question is to redesign the algorithm to operate in a supervised manner. More specifically, we propose an end-to-end deep architecture with the exact K-SVD computational path, and train it for optimized denoising. Our work shows how to overcome difficulties arising in turning the K-SVD scheme into a differentiable, and thus learnable, machine. With a small number of parameters to learn and while preserving the original K-SVD essence, the proposed architecture is shown to outperform the classical K-SVD algorithm substantially, and getting closer to recent state-of-the-art learning-based denoising methods. Adopting a broader context, this work touches on themes around the design of deep-learning solutions for image processing tasks, while paving a bridge between classic methods and novel deep-learning-based ones.
这项工作考虑从图像中去除噪声，重点关注著名的K-SVD去噪算法。这种基于稀疏性的方法于2006年被提出，在短时间内被认为是最先进的。然而，多年来它已被其他方法超越，包括最近基于深度学习的新方法。我们在本文中解决的问题是，K-SVD在其最初的概念中是否已达到顶峰，或者它是否可以再次具有竞争力。我们回答这个问题所采用的方法是重新设计算法，使其以监督方式运行。更具体地说，我们提出了一种具有精确K-SVD计算路径的端到端深度架构，并对其进行训练以实现优化去噪。我们的工作展示了如何克服将K-SVD方案转变为可微且因此可学习的机器时出现的困难。通过学习少量参数并在保留原始K-SVD本质的同时，所提出的架构被证明大大优于经典K-SVD算法，并更接近最近基于学习的最先进去噪方法。从更广泛的背景来看，这项工作涉及围绕图像处理任务的深度学习解决方案设计的主题，同时在经典方法和基于深度学习创新方法之间架起一座桥梁。
