Although there have been significant advances in the field of image
restoration recently, the system complexity of the state-of-the-art (SOTA)
methods is increasing as well, which may hinder the convenient analysis and
comparison of methods. In this paper, we propose a simple baseline that exceeds
the SOTA methods and is computationally efficient. To further simplify the
baseline, we reveal that the nonlinear activation functions, e.g. Sigmoid,
ReLU, GELU, Softmax, etc. are not necessary: they could be replaced by
multiplication or removed. Thus, we derive a Nonlinear Activation Free Network,
namely NAFNet, from the baseline. SOTA results are achieved on various
challenging benchmarks, e.g. 33.69 dB PSNR on GoPro (for image deblurring),
exceeding the previous SOTA 0.38 dB with only 8.4% of its computational costs;
40.30 dB PSNR on SIDD (for image denoising), exceeding the previous SOTA 0.28
dB with less than half of its computational costs. The code and the pre-trained
models are released at this https URL

本篇论文提出了一种不需要非线性激活函数的图像恢复模型 NAFNet，其计算效率比现有 SOTA 方法高，且在多个挑战认可的基准测试中获得了最佳结果。

图像恢复的简单基线

Simple Baselines for Image Restoration

We introduce a new framework for learning dense correspondence between
deformable 3D shapes. Existing learning based approaches model shape
correspondence as a labelling problem, where each point of a query shape
receives a label identifying a point on some reference domain; the
correspondence is then constructed a posteriori by composing the label
predictions of two input shapes. We propose a paradigm shift and design a
structured prediction model in the space of functional maps, linear operators
that provide a compact representation of the correspondence. We model the
learning process via a deep residual network which takes dense descriptor
fields defined on two shapes as input, and outputs a soft map between the two
given objects. The resulting correspondence is shown to be accurate on several
challenging benchmarks comprising multiple categories, synthetic models, real
scans with acquisition artifacts, topological noise, and partiality.

该研究提出了一种新的学习框架，用于学习变形的 3D 形状之间的密集对应关系，通过使用一个在功能映射空间内的结构化预测模型，以及对两个形状定义的密集描述符场的输入和输出，得到在多个具有挑战性的基准测试中表现准确的对应关系。