Image restoration, or inverse problems in image processing, has long been an
extensively studied topic. In recent years supervised learning approaches have
become a popular strategy attempting to tackle this task. Unfortunately, most
supervised learning-based methods are highly demanding in terms of
computational resources and training data (sample complexity). In addition,
trained models are sensitive to domain changes, such as varying acquisition
systems, signal sampling rates, resolution and contrast. In this work, we try
to answer a fundamental question: Can supervised learning models generalize
well solely by learning from one image or even part of an image? If so, then
what is the minimal amount of patches required to achieve acceptable
generalization? To this end, we focus on an efficient patch-based learning
framework that requires a single image input-output pair for training.
Experimental results demonstrate the applicability, robustness and
computational efficiency of the proposed approach for supervised image
deblurring and super-resolution. Our results showcase significant improvement
of learning models' sample efficiency, generalization and time complexity, that
can hopefully be leveraged for future real-time applications, and applied to
other signals and modalities.

本文提出了一种高效的基于图像补丁学习的框架，仅需一个图像输入输出对进行训练，实验证明该方法适用于监督式图像去模糊和超分辨率任务，并显著提高了学习模型的样本效率、泛化能力和时间复杂度。

一次性图像修复

One-Shot Image Restoration

A key step in any scanning-based asset creation workflow is to convert
unordered point clouds to a surface. Classical methods (e.g., Poisson
reconstruction) start to degrade in the presence of noisy and partial scans.
Hence, deep learning based methods have recently been proposed to produce
complete surfaces, even from partial scans. However, such data-driven methods
struggle to generalize to new shapes with large geometric and topological
variations. We present Points2Surf, a novel patch-based learning framework that
produces accurate surfaces directly from raw scans without normals. Learning a
prior over a combination of detailed local patches and coarse global
information improves generalization performance and reconstruction accuracy.
Our extensive comparison on both synthetic and real data demonstrates a clear
advantage of our method over state-of-the-art alternatives on previously unseen
classes (on average, Points2Surf brings down reconstruction error by 30\% over
SPR and by 270\%+ over deep learning based SotA methods) at the cost of longer
computation times and a slight increase in small-scale topological noise in
some cases. Our source code, pre-trained model, and dataset are available on:
this https URL

本研究提出了 Points2Surf 算法，一种基于深度学习框架的局部深度信息和整体粗略信息相结合的面重建方法，显著提高了模型泛化能力和重建精度。实验证明，与其他方法相比较，Points2Surf 算法能够在新的形状上实现高质量的重建，并能使重建误差降低 30% 以上。