Consistency and realness have always been the two critical issues of image
super-resolution. While the realness has been dramatically improved with the
use of GAN prior, the state-of-the-art methods still suffer inconsistencies in
local structures and colors (e.g., tooth and eyes). In this paper, we show that
these inconsistencies can be analytically eliminated by learning only the
null-space component while fixing the range-space part. Further, we design a
pooling-based decomposition (PD), a universal range-null space decomposition
for super-resolution tasks, which is concise, fast, and parameter-free. PD can
be easily applied to state-of-the-art GAN Prior based SR methods to eliminate
their inconsistencies, neither compromising the realness nor bringing extra
parameters or computational costs. Besides, our ablation studies reveal that PD
can replace pixel-wise losses for training and achieve better generalization
performance when facing unseen downsamplings or even real-world degradation.
Experiments show that the use of PD refreshes state-of-the-art SR performance
and speeds up the convergence of training up to 2~10 times.

通过学习零空间部分和固定范围空间，使用基于池化的分解方法（PD）来消除现有风格迁移 SR 方法中颜色、局部结构的一致性问题，并在面对未知下采样和现实世界退化时取得更好的泛化性能。

基于 GAN 先验的零空间学习用于一致性超分辨率

GAN Prior based Null-Space Learning for Consistent Super-Resolution

Emerging high-quality face restoration (FR) methods often utilize pre-trained
GAN models (\textit{i.e.}, StyleGAN2) as GAN Prior. However, these methods
usually struggle to balance realness and fidelity when facing various
degradation levels. Besides, there is still a noticeable visual quality gap
compared with pre-trained GAN models. In this paper, we propose a novel GAN
Prior based degradation-aware feature interpolation network, dubbed Panini-Net,
for FR tasks by explicitly learning the abstract representations to distinguish
various degradations. Specifically, an unsupervised degradation representation
learning (UDRL) strategy is first developed to extract degradation
representations (DR) of the input degraded images. Then, a degradation-aware
feature interpolation (DAFI) module is proposed to dynamically fuse the two
types of informative features (\textit{i.e.}, features from input images and
features from GAN Prior) with flexible adaption to various degradations based
on DR. Ablation studies reveal the working mechanism of DAFI and its potential
for editable FR. Extensive experiments demonstrate that our Panini-Net achieves
state-of-the-art performance for multi-degradation face restoration and face
super-resolution. The source code is available at
this https URL

本文中，我们提出了一种基于降级感知特征插值网络的 GAN 先验方法，命名为 Panini-Net，用于面部恢复任务，可实现多种降级图像的面部恢复和面部超分辨率，实验证明其获得了业内领先的性能。

Panini-Net: 基于 GAN 先验的面部修复特征插值算法

Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for  Face Restoration

Face image super resolution (face hallucination) usually relies on facial
priors to restore realistic details and preserve identity information. Recent
advances can achieve impressive results with the help of GAN prior. They either
design complicated modules to modify the fixed GAN prior or adopt complex
training strategies to finetune the generator. In this work, we propose a
generative and controllable face SR framework, called GCFSR, which can
reconstruct images with faithful identity information without any additional
priors. Generally, GCFSR has an encoder-generator architecture. Two modules
called style modulation and feature modulation are designed for the
multi-factor SR task. The style modulation aims to generate realistic face
details and the feature modulation dynamically fuses the multi-level encoded
features and the generated ones conditioned on the upscaling factor. The simple
and elegant architecture can be trained from scratch in an end-to-end manner.
For small upscaling factors (<=8), GCFSR can produce surprisingly good results
with only adversarial loss. After adding L1 and perceptual losses, GCFSR can
outperform state-of-the-art methods for large upscaling factors (16, 32, 64).
During the test phase, we can modulate the generative strength via feature
modulation by changing the conditional upscaling factor continuously to achieve
various generative effects.

本文介绍了一种基于 GAN 先验的面部图像超分辨率方法，通过特征调制和风格调制生成更加真实的面部细节并保留识别信息，对比现有方法，该方法可以在不需要额外先验的情况下从头开始训练，并可在测试时通过特征调制实现不同的生成效果。