Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image
  Restoration
Akshay Dudhane Omkar Thawakar Syed Waqas Zamir Salman Khan Fahad Shahbaz Khan Ming-Hsuan Yang
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 https://github.com/akshaydudhane16/DyNet.