All-in-one image restoration tackles different types of degradations with a
unified model instead of having task-specific, non-generic models for each
degradation. The requirement to tackle multiple degradations using the same
model can lead to high-complexity designs with fixed configuration that lack
the adaptability to more efficient alternatives. We propose DyNet, a dynamic
family of networks designed in an encoder-decoder style for all-in-one image
restoration tasks. Our DyNet can seamlessly switch between its bulkier and
lightweight variants, thereby offering flexibility for efficient model
deployment with a single round of training. This seamless switching is enabled
by our weights-sharing mechanism, forming the core of our architecture and
facilitating the reuse of initialized module weights. Further, to establish
robust weights initialization, we introduce a dynamic pre-training strategy
that trains variants of the proposed DyNet concurrently, thereby achieving a
50% reduction in GPU hours. To tackle the unavailability of large-scale dataset
required in pre-training, we curate a high-quality, high-resolution image
dataset named Million-IRD having 2M image samples. We validate our DyNet for
image denoising, deraining, and dehazing in all-in-one setting, achieving
state-of-the-art results with 31.34% reduction in GFlops and a 56.75% reduction
in parameters compared to baseline models. The source codes and trained models
are available at this https URL

通过 DyNet 动态网络设计的全能图像恢复任务模型，结合权重共享机制和动态预训练策略，在图像去噪、去雨和去雾等多种任务上取得了最新的成果，同时比基准模型减少 31.34% 的 GFlops 与 56.75% 的参数。

动态预训练：高效可扩展的一体化图像恢复

Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image  Restoration

We describe DyNet, a toolkit for implementing neural network models based on
dynamic declaration of network structure. In the static declaration strategy
that is used in toolkits like Theano, CNTK, and TensorFlow, the user first
defines a computation graph (a symbolic representation of the computation), and
then examples are fed into an engine that executes this computation and
computes its derivatives. In DyNet's dynamic declaration strategy, computation
graph construction is mostly transparent, being implicitly constructed by
executing procedural code that computes the network outputs, and the user is
free to use different network structures for each input. Dynamic declaration
thus facilitates the implementation of more complicated network architectures,
and DyNet is specifically designed to allow users to implement their models in
a way that is idiomatic in their preferred programming language (C++ or
Python). One challenge with dynamic declaration is that because the symbolic
computation graph is defined anew for every training example, its construction
must have low overhead. To achieve this, DyNet has an optimized C++ backend and
lightweight graph representation. Experiments show that DyNet's speeds are
faster than or comparable with static declaration toolkits, and significantly
faster than Chainer, another dynamic declaration toolkit. DyNet is released
open-source under the Apache 2.0 license and available at
this http URL

DyNet 是一个基于动态声明网络结构的神经网络模型工具包，其动态声明策略通过执行计算网络输出的程序代码来隐式构建计算图，使得实现更复杂的网络体系结构变得更加容易，并且通过优化的 C++ 后端和轻量级图表示实现低开销的计算图构建。