1,367 research outputs found
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond
Underwater images suffer from light refraction and absorption, which impairs
visibility and interferes the subsequent applications. Existing underwater
image enhancement methods mainly focus on image quality improvement, ignoring
the effect on practice. To balance the visual quality and application, we
propose a heuristic normalizing flow for detection-driven underwater image
enhancement, dubbed WaterFlow. Specifically, we first develop an invertible
mapping to achieve the translation between the degraded image and its clear
counterpart. Considering the differentiability and interpretability, we
incorporate the heuristic prior into the data-driven mapping procedure, where
the ambient light and medium transmission coefficient benefit credible
generation. Furthermore, we introduce a detection perception module to transmit
the implicit semantic guidance into the enhancement procedure, where the
enhanced images hold more detection-favorable features and are able to promote
the detection performance. Extensive experiments prove the superiority of our
WaterFlow, against state-of-the-art methods quantitatively and qualitatively.Comment: 10 pages, 13 figure
Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network
Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network’s generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What’more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies
Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey
Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research
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
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