Underwater Image Enhancement by Transformer-based Diffusion Model with
  Non-uniform Sampling for Skip Strategy
Yi Tang Takafumi Iwaguchi Hiroshi Kawasaki
In this paper, we present an approach to image enhancement with diffusion
model in underwater scenes. Our method adapts conditional denoising diffusion
probabilistic models to generate the corresponding enhanced images by using the
underwater images and the Gaussian noise as the inputs. Additionally, in order
to improve the efficiency of the reverse process in the diffusion model, we
adopt two different ways. We firstly propose a lightweight transformer-based
denoising network, which can effectively promote the time of network forward
per iteration. On the other hand, we introduce a skip sampling strategy to
reduce the number of iterations. Besides, based on the skip sampling strategy,
we propose two different non-uniform sampling methods for the sequence of the
time step, namely piecewise sampling and searching with the evolutionary
algorithm. Both of them are effective and can further improve performance by
using the same steps against the previous uniform sampling. In the end, we
conduct a relative evaluation of the widely used underwater enhancement
datasets between the recent state-of-the-art methods and the proposed approach.
The experimental results prove that our approach can achieve both competitive
performance and high efficiency. Our code is available at
\href{mailto:https://github.com/piggy2009/DM_underwater}{\color{blue}{https://github.com/piggy2009/DM\_underwater}}.