A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning

• URL: http://arxiv.org/abs/2405.19684v3
• Date: Wed, 26 Jun 2024 03:28:15 GMT
• Title: A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning
• Authors: Xiaofeng Cong, Yu Zhao, Jie Gui, Junming Hou, Dacheng Tao,
• Abstract summary: Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
• Score: 51.7818820745221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent. To foster future advancements, we provide a detailed overview of the UIE task from several perspectives. Firstly, we introduce the physical models, data construction processes, evaluation metrics, and loss functions. Secondly, we categorize and discuss recent algorithms based on their contributions, considering six aspects: network architecture, learning strategy, learning stage, auxiliary tasks, domain perspective, and disentanglement fusion. Thirdly, due to the varying experimental setups in the existing literature, a comprehensive and unbiased comparison is currently unavailable. To address this, we perform both quantitative and qualitative evaluations of state-of-the-art algorithms across multiple benchmark datasets. Lastly, we identify key areas for future research in UIE. A collection of resources for UIE can be found at {https://github.com/YuZhao1999/UIE}.

Related papers

• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends [1.124958340749622]
  This paper reviews prominent research in Understanding (CU) It focuses on State-of-The-Art (SoTA) frameworks that employ transformers within End-to-End (E2E) solutions. This article identifies key challenges and outlines promising future directions for advancing CU solutions.
  arXiv  Detail & Related papers  (2024-10-05T16:26:44Z)
• A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application [7.595948982978669]
  Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. We present a review of Continual semantic segmentation (CSS), of which the dense prediction peculiarity makes it a challenging, intricate and burgeoning task. We sort out and categorize current CSS models into two main branches including data-replay and data-free sets. We also introduce four CSS specialities with diverse application scenarios and development tendencies.
  arXiv  Detail & Related papers  (2023-10-22T11:53:56Z)
• Deep Depth Completion: A Survey [26.09557446012222]
  We provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies. We present a quantitative comparison of model performance on two widely used benchmark datasets, including an indoor and an outdoor dataset.
  arXiv  Detail & Related papers  (2022-05-11T08:24:00Z)
• Fine-Grained Image Analysis with Deep Learning: A Survey [146.22351342315233]
  Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition. This paper attempts to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval.
  arXiv  Detail & Related papers  (2021-11-11T09:43:56Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• A Comprehensive Survey on Image Dehazing Based on Deep Learning [89.77554550654227]
  The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. There are few studies that summarize the deep learning (DL) based dehazing technologies.
  arXiv  Detail & Related papers  (2021-06-07T03:51:25Z)
• Deep Image Retrieval: A Survey [21.209884703192735]
  We focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of category-based CBIR.
  arXiv  Detail & Related papers  (2021-01-27T09:32:58Z)
• Deep Learning for Person Re-identification: A Survey and Outlook [233.36948173686602]
  Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings.
  arXiv  Detail & Related papers  (2020-01-13T12:49:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.