521 research outputs found
Is Underwater Image Enhancement All Object Detectors Need?
Underwater object detection is a crucial and challenging problem in marine
engineering and aquatic robot. The difficulty is partly because of the
degradation of underwater images caused by light selective absorption and
scattering. Intuitively, enhancing underwater images can benefit high-level
applications like underwater object detection. However, it is still unclear
whether all object detectors need underwater image enhancement as
pre-processing. We therefore pose the questions "Does underwater image
enhancement really improve underwater object detection?" and "How does
underwater image enhancement contribute to underwater object detection?". With
these two questions, we conduct extensive studies. Specifically, we use 18
state-of-the-art underwater image enhancement algorithms, covering traditional,
CNN-based, and GAN-based algorithms, to pre-process underwater object detection
data. Then, we retrain 7 popular deep learning-based object detectors using the
corresponding results enhanced by different algorithms, obtaining 126
underwater object detection models. Coupled with 7 object detection models
retrained using raw underwater images, we employ these 133 models to
comprehensively analyze the effect of underwater image enhancement on
underwater object detection. We expect this study can provide sufficient
exploration to answer the aforementioned questions and draw more attention of
the community to the joint problem of underwater image enhancement and
underwater object detection. The pre-trained models and results are publicly
available and will be regularly updated. Project page:
https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/uw_enhancement_affect_detection.Comment: 17 pages, 9 figure
PRIDNet based Image Denoising for Underwater Images
Underwater image enhancement has become a popular research topic due to its importance in aquatic robotics and marine engineering. However, the underwater images frequently experience signal-dependent speckle noise when transmitting and acquiring data, which can limit certain applications such as detection, object tracking. In the recent years, the existing underwater image enhancement algorithms efficiency has been analysed and evaluated on a small number of carefully chosen real-world images or synthetic datasets. As such, it is challenging to predict how these algorithms might function with images acquired in the wild under various circumstances. This paper introduces a new solution for noise removal from underwater images called Pyramid Real Image Noise Removal Network (PRIDNet) with patches.PRIDNet is a three-level network design using image patches. The tests were carried out on a dataset of actual noisy images demonstrate that, in terms of quantitative metrics, our proposed denoising model reduction performs better with the exixting denoisers. We determine the effectiveness and constraints of existing algorithms using benchmark assessments and the suggested model, offering valuable information for further studies on underwater image enhancement
Physics-Aware Semi-Supervised Underwater Image Enhancement
Underwater images normally suffer from degradation due to the transmission
medium of water bodies. Both traditional prior-based approaches and deep
learning-based methods have been used to address this problem. However, the
inflexible assumption of the former often impairs their effectiveness in
handling diverse underwater scenes, while the generalization of the latter to
unseen images is usually weakened by insufficient data. In this study, we
leverage both the physics-based underwater Image Formation Model (IFM) and deep
learning techniques for Underwater Image Enhancement (UIE). To this end, we
propose a novel Physics-Aware Dual-Stream Underwater Image Enhancement Network,
i.e., PA-UIENet, which comprises a Transmission Estimation Steam (T-Stream) and
an Ambient Light Estimation Stream (A-Stream). This network fulfills the UIE
task by explicitly estimating the degradation parameters of the IFM. We also
adopt an IFM-inspired semi-supervised learning framework, which exploits both
the labeled and unlabeled images, to address the issue of insufficient data.
Our method performs better than, or at least comparably to, eight baselines
across five testing sets in the degradation estimation and UIE tasks. This
should be due to the fact that it not only can model the degradation but also
can learn the characteristics of diverse underwater scenes.Comment: 12 pages, 5 figure
- …