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}{this https URL}}.

通过在水下场景中使用扩散模型进行图像增强的方法，本文提出了一种方法。我们的方法利用水下图像和高斯噪声作为输入，采用条件降噪扩散概率模型生成相应的增强图像。此外，为了提高扩散模型中的反向过程的效率，我们采用了两种不同的方式。我们首先提出了一种基于轻量级 Transformer 的降噪网络，可以有效提升每次迭代的网络正向时间。另外，我们引入了一种跳跃抽样策略来减少迭代次数。此外，在跳跃抽样策略的基础上，我们提出了两种不同的非均匀抽样方法，即分段抽样和使用进化算法进行搜索。它们都是有效的，并且通过相同的步骤针对以前的均匀抽样进一步提高性能。最后，我们对最近最先进的水下增强数据集和提出的方法进行了相对评估。实验结果证明，我们的方法既能取得竞争性的性能，又具备高效性。我们的代码可在 https://github.com/piggy2009/DM_underwater 中获得。

基于 Transformer 的扩散模型的非均匀采样用于跳跃策略的水下图像增强

Underwater Image Enhancement by Transformer-based Diffusion Model with  Non-uniform Sampling for Skip Strategy

We present RGB-D-Fusion, a multi-modal conditional denoising diffusion
probabilistic model to generate high resolution depth maps from low-resolution
monocular RGB images of humanoid subjects. RGB-D-Fusion first generates a
low-resolution depth map using an image conditioned denoising diffusion
probabilistic model and then upsamples the depth map using a second denoising
diffusion probabilistic model conditioned on a low-resolution RGB-D image. We
further introduce a novel augmentation technique, depth noise augmentation, to
increase the robustness of our super-resolution model.

RGB-D-Fusion 模型使用条件去噪扩散概率模型从人类主体的低分辨率单眼 RGB 图像生成高分辨率深度图，并引入深度噪声增强技术以提升超分辨率模型的鲁棒性。