Image restoration tasks demand a complex balance between spatial details and
high-level contextualized information while recovering images. In this paper,
we propose a novel synergistic design that can optimally balance these
competing goals. Our main proposal is a multi-stage architecture, that
progressively learns restoration functions for the degraded inputs, thereby
breaking down the overall recovery process into more manageable steps.
Specifically, our model first learns the contextualized features using
encoder-decoder architectures and later combines them with a high-resolution
branch that retains local information. At each stage, we introduce a novel
per-pixel adaptive design that leverages in-situ supervised attention to
reweight the local features. A key ingredient in such a multi-stage
architecture is the information exchange between different stages. To this end,
we propose a two-faceted approach where the information is not only exchanged
sequentially from early to late stages, but lateral connections between feature
processing blocks also exist to avoid any loss of information. The resulting
tightly interlinked multi-stage architecture, named as MPRNet, delivers strong
performance gains on ten datasets across a range of tasks including image
deraining, deblurring, and denoising. The source code and pre-trained models
are available at this https URL

该研究提出了一种名为 MPRNet 的多阶段架构，使用编码器 - 解码器架构学习上下文信息和高分辨率分支，结合像素自适应设计重新加权本地特征，从而实现复原图像的同时维持空间细节和上下文信息的最佳平衡，该架构在包括图像去雨、去模糊和降噪等多个任务上均表现出较强的性能表现。

多阶段渐进式图像修复

Multi-Stage Progressive Image Restoration

The latest work on language representations carefully integrates
contextualized features into language model training, which enables a series of
success especially in various machine reading comprehension and natural
language inference tasks. However, the existing language representation models
including ELMo, GPT and BERT only exploit plain context-sensitive features such
as character or word embeddings. They rarely consider incorporating structured
semantic information which can provide rich semantics for language
representation. To promote natural language understanding, we propose to
incorporate explicit contextual semantics from pre-trained semantic role
labeling, and introduce an improved language representation model,
Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing
contextual semantics over a BERT backbone. SemBERT keeps the convenient
usability of its BERT precursor in a light fine-tuning way without substantial
task-specific modifications. Compared with BERT, semantics-aware BERT is as
simple in concept but more powerful. It obtains new state-of-the-art or
substantially improves results on ten reading comprehension and language
inference tasks.

提出了一种改进的语言表示模型 Semantics-aware BERT，通过集成来自预训练语义角色标注的显式上下文语义，从而在十项阅读理解和语言推理任务中获得了新的最先进或实质性改进的结果，以促进自然语言理解。