Underwater Image Enhancement (UIE) is critical for marine research and
exploration but hindered by complex color distortions and severe blurring.
Recent deep learning-based methods have achieved remarkable results, yet these
methods struggle with high computational costs and insufficient global
modeling, resulting in locally under- or over- adjusted regions. We present
PixMamba, a novel architecture, designed to overcome these challenges by
leveraging State Space Models (SSMs) for efficient global dependency modeling.
Unlike convolutional neural networks (CNNs) with limited receptive fields and
transformer networks with high computational costs, PixMamba efficiently
captures global contextual information while maintaining computational
efficiency. Our dual-level strategy features the patch-level Efficient Mamba
Net (EMNet) for reconstructing enhanced image feature and the pixel-level
PixMamba Net (PixNet) to ensure fine-grained feature capturing and global
consistency of enhanced image that were previously difficult to obtain.
PixMamba achieves state-of-the-art performance across various underwater image
datasets and delivers visually superior results. Code is available at:
this https URL

使用 PixMamba 和 State Space Models，PixMamba 能够有效地处理水下图像增强的问题，提供了全局依赖建模的能力，并通过 EMNet 和 PixNet 两级策略实现了高效的全局上下文信息获取和视觉上更好的结果。

PixMamba: 在双层架构中利用状态空间模型进行水下图像增强

PixMamba: Leveraging State Space Models in a Dual-Level Architecture for  Underwater Image Enhancement

Underwater video enhancement (UVE) aims to improve the visibility and frame
quality of underwater videos, which has significant implications for marine
research and exploration. However, existing methods primarily focus on
developing image enhancement algorithms to enhance each frame independently.
There is a lack of supervised datasets and models specifically tailored for UVE
tasks. To fill this gap, we construct the Synthetic Underwater Video
Enhancement (SUVE) dataset, comprising 840 diverse underwater-style videos
paired with ground-truth reference videos. Based on this dataset, we train a
novel underwater video enhancement model, UVENet, which utilizes inter-frame
relationships to achieve better enhancement performance. Through extensive
experiments on both synthetic and real underwater videos, we demonstrate the
effectiveness of our approach. This study represents the first comprehensive
exploration of UVE to our knowledge. The code is available at
this https URL

基于构建的合成水下视频增强数据集 (SUVE) 和利用帧间关系的新型水下视频增强模型 UVENet，该研究首次全面探索水下视频增强领域，并在合成和真实水下视频的广泛实验中展示了方法的有效性。

端到端水下视频增强：数据集与模型

End-To-End Underwater Video Enhancement: Dataset and Model

Photo-identification (photo-id) is one of the main non-invasive
capture-recapture methods utilised by marine researchers for monitoring
cetacean (dolphin, whale, and porpoise) populations. This method has
historically been performed manually resulting in high workload and cost due to
the vast number of images collected. Recently automated aids have been
developed to help speed-up photo-id, although they are often disjoint in their
processing and do not utilise all available identifying information. Work
presented in this paper aims to create a fully automatic photo-id aid capable
of providing most likely matches based on all available information without the
need for data pre-processing such as cropping. This is achieved through a
pipeline of computer vision models and post-processing techniques aimed at
detecting cetaceans in unedited field imagery before passing them downstream
for individual level catalogue matching. The system is capable of handling
previously uncatalogued individuals and flagging these for investigation thanks
to catalogue similarity comparison. We evaluate the system against multiple
real-life photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for the task
of dorsal fin detection on catalogues from Tanzania and the UK respectively and
83.1, 97.5% top-10 accuracy for the task of individual classification on
catalogues from the UK and USA.

本文提出使用计算机视觉模型和后处理技术来创建一个完全自动的 Photo-identification (photo-id) 识别系统，该系统可以根据所有可用信息提供最可能的匹配，无需数据预处理，用于监测海洋中哺乳动物种群。系统通过目录相似性比较，能够处理以前未编目的个体，并标记这些个体以进行进一步调查。该文研究表明，系统在多个现实生活中的 Photo-id 相册上的表现非常好。