Due to their highly structured characteristics, faces are easier to recover
than natural scenes for blind image super-resolution. Therefore, we can extract
the degradation representation of an image from the low-quality and recovered
face pairs. Using the degradation representation, realistic low-quality images
can then be synthesized to fine-tune the super-resolution model for the
real-world low-quality image. However, such a procedure is time-consuming and
laborious, and the gaps between recovered faces and the ground-truths further
increase the optimization uncertainty. To facilitate efficient model adaptation
towards image-specific degradations, we propose a method dubbed MetaF2N, which
leverages the contained Faces to fine-tune model parameters for adapting to the
whole Natural image in a Meta-learning framework. The degradation extraction
and low-quality image synthesis steps are thus circumvented in our MetaF2N, and
it requires only one fine-tuning step to get decent performance. Considering
the gaps between the recovered faces and ground-truths, we further deploy a
MaskNet for adaptively predicting loss weights at different positions to reduce
the impact of low-confidence areas. To evaluate our proposed MetaF2N, we have
collected a real-world low-quality dataset with one or multiple faces in each
image, and our MetaF2N achieves superior performance on both synthetic and
real-world datasets. Source code, pre-trained models, and collected datasets
are available at this https URL

通过 MetaF2N 方法，利用包含的人脸来微调超分辨率模型参数以适应整个自然图像，在去除降级表征和低质量图像合成步骤的同时，只需一次微调步骤即可获得良好的性能。

MetaF2N：基于学习高效模型自适应的盲目人脸图像超分辨率

MetaF2N: Blind Image Super-Resolution by Learning Efficient Model  Adaptation from Faces

Since rain streaks show a variety of shapes and directions, learning the
degradation representation is extremely challenging for single image deraining.
Existing methods are mainly targeted at designing complicated modules to
implicitly learn latent degradation representation from coupled rainy images.
This way, it is hard to decouple the content-independent degradation
representation due to the lack of explicit constraint, resulting in over- or
under-enhancement problems. To tackle this issue, we propose a novel Latent
Degradation Representation Constraint Network (LDRCNet) that consists of
Direction-Aware Encoder (DAEncoder), UNet Deraining Network, and Multi-Scale
Interaction Block (MSIBlock). Specifically, the DAEncoder is proposed to
adaptively extract latent degradation representation by using the deformable
convolutions to exploit the direction consistency of rain streaks. Next, a
constraint loss is introduced to explicitly constraint the degradation
representation learning during training. Last, we propose an MSIBlock to fuse
with the learned degradation representation and decoder features of the
deraining network for adaptive information interaction, which enables the
deraining network to remove various complicated rainy patterns and reconstruct
image details. Experimental results on synthetic and real datasets demonstrate
that our method achieves new state-of-the-art performance.

通过引入方向感知编码器（DAEncoder）、UNet 去雨网络和多尺度交互块（MSIBlock），该论文提出一种新颖的潜在退化表示约束网络（LDRCNet），用于自适应地提取潜在的退化表示、显式约束退化表示学习，并通过信息交互实现各种复杂雨滴模式的去除和图像细节的重构，实验结果表明我们的方法达到了最新的最优性能。

单幅图像去雨的潜在退化表示约束

Latent Degradation Representation Constraint for Single Image Deraining

The aim of image restoration is to recover high-quality images from distorted
ones. However, current methods usually focus on a single task (\emph{e.g.},
denoising, deblurring or super-resolution) which cannot address the needs of
real-world multi-task processing, especially on mobile devices. Thus,
developing an all-in-one method that can restore images from various unknown
distortions is a significant challenge. Previous works have employed
contrastive learning to learn the degradation representation from observed
images, but this often leads to representation drift caused by deficient
positive and negative pairs. To address this issue, we propose a novel
All-in-one Multi-degradation Image Restoration Network (AMIRNet) that can
effectively capture and utilize accurate degradation representation for image
restoration. AMIRNet learns a degradation representation for unknown degraded
images by progressively constructing a tree structure through clustering,
without any prior knowledge of degradation information. This tree-structured
representation explicitly reflects the consistency and discrepancy of various
distortions, providing a specific clue for image restoration. To further
enhance the performance of the image restoration network and overcome domain
gaps caused by unknown distortions, we design a feature transform block (FTB)
that aligns domains and refines features with the guidance of the degradation
representation. We conduct extensive experiments on multiple distorted
datasets, demonstrating the effectiveness of our method and its advantages over
state-of-the-art restoration methods both qualitatively and quantitatively.

我们提出了一种全能多退化图像恢复网络 (AMIRNet)，通过聚类逐步构建树状结构，学习未知退化图像的退化表示，并利用特征转换模块 (FTB) 弥补领域差异，从而实现从各种未知失真中恢复高质量图像的目标。