Physics-Inspired Synthesized Underwater Image Dataset

• URL: http://arxiv.org/abs/2404.03998v2
• Date: Wed, 11 Dec 2024 10:52:47 GMT
• Title: Physics-Inspired Synthesized Underwater Image Dataset
• Authors: Reina Kaneko, Takumi Ueda, Hiroshi Higashi, Yuichi Tanaka,
• Abstract summary: PHISWID is a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis.<n>Our dataset contributes to the development in underwater image processing.
• Score: 9.117162374919715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces the physics-inspired synthesized underwater image dataset (PHISWID), a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis. For underwater image enhancement, data-driven approaches (e.g., deep neural networks) typically demand extensive datasets, yet acquiring paired clean and degraded underwater images poses significant challenges. Existing datasets have limited contributions to image enhancement due to lack of physics models, publicity, and ground-truth images. PHISWID addresses these issues by offering a set of paired ground-truth (atmospheric) and underwater images synthetically degraded by color degradation and marine snow artifacts. Generating underwater images from atmospheric RGB-D images based on physical models provides pairs of real-world ground-truth and degraded images. Our synthetic approach generates a large quantity of the pairs, enabling effective training of deep neural networks and objective image quality assessment. Through benchmark experiment with some datasets and image enhance methods, we validate that our dataset can improve the image enhancement performance. Our dataset, which is publicly available, contributes to the development in underwater image processing.

Related papers

• AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis [57.249817395828174]
  We propose a scalable framework combining pseudo-synthetic renderings from 3D city-wide meshes with real, ground-level crowd-sourced images. The pseudo-synthetic data simulates a wide range of aerial viewpoints, while the real, crowd-sourced images help improve visual fidelity for ground-level images. Using this hybrid dataset, we fine-tune several state-of-the-art algorithms and achieve significant improvements on real-world, zero-shot aerial-ground tasks.
  arXiv  Detail & Related papers  (2025-04-17T17:57:05Z)
• DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancement [2.1953477234116705]
  This research presents a two-stage underwater image enhancement network called the Data-Driven and Physical Parameters Fusion Network (DPF-Net) It harnesses the robustness of physical imaging models alongside the generality and efficiency of data-driven methods. Our proposed DPF-Net demonstrates superior performance compared to other benchmark methods across multiple test sets.
  arXiv  Detail & Related papers  (2025-03-16T11:53:18Z)
• Underwater Image Enhancement using Generative Adversarial Networks: A Survey [1.2582887633807602]
  Generative Adversarial Networks (GANs) have emerged as a powerful tool for enhancing underwater photos. GANs have been applied to real-world applications, including marine biology and ecosystem monitoring, coral reef health assessment, underwater archaeology, and autonomous underwater vehicle (AUV) navigation. This paper explores all major approaches to underwater image enhancement, from physical and physics-free models to CNN-based models and state-of-the-art GAN-based methods.
  arXiv  Detail & Related papers  (2025-01-10T06:41:19Z)
• UW-SDF: Exploiting Hybrid Geometric Priors for Neural SDF Reconstruction from Underwater Multi-view Monocular Images [63.32490897641344]
  We propose a framework for reconstructing target objects from multi-view underwater images based on neural SDF. We introduce hybrid geometric priors to optimize the reconstruction process, markedly enhancing the quality and efficiency of neural SDF reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T16:33:56Z)
• Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method [77.80712860663886]
  4-D light fields (LFs) enhance underwater imaging plagued by light absorption, scattering, and other challenges. We propose a progressive framework for underwater 4-D LF image enhancement and depth estimation. We construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods.
  arXiv  Detail & Related papers  (2024-08-30T15:06:45Z)
• Physics Informed and Data Driven Simulation of Underwater Images via Residual Learning [5.095097384893417]
  In general, underwater images suffer from color distortion and low contrast, because light is attenuated and backscattered as it propagates through water. An existing simple degradation model (similar to atmospheric image "hazing" effects) is not sufficient to properly represent the underwater image degradation. We propose a deep learning-based architecture to automatically simulate the underwater effects.
  arXiv  Detail & Related papers  (2024-02-07T21:53:28Z)
• Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion [30.122666238416716]
  We propose a novel pipeline for generating underwater images using accurate terrestrial depth data. This approach facilitates the training of supervised models for underwater depth estimation. We introduce a unique Depth2Underwater ControlNet, trained on specially prepared Underwater, Depth, Text data triplets.
  arXiv  Detail & Related papers  (2023-12-19T08:56:33Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators [120.06891448820447]
  How to obtain clear and visually pleasant images has become a common concern of people. The task of underwater image enhancement (UIE) has also emerged as the times require. In this paper, we propose a physical model-guided GAN model for UIE, referred to as PUGAN. Our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics.
  arXiv  Detail & Related papers  (2023-06-15T07:41:12Z)
• MetaUE: Model-based Meta-learning for Underwater Image Enhancement [25.174894007563374]
  This paper proposes a model-based deep learning method for restoring clean images under various underwater scenarios. The meta-learning strategy is used to obtain a pre-trained model on the synthetic underwater dataset. The model is then fine-tuned on real underwater datasets to obtain a reliable underwater image enhancement model, called MetaUE.
  arXiv  Detail & Related papers  (2023-03-12T02:38:50Z)
• WaterNeRF: Neural Radiance Fields for Underwater Scenes [6.161668246821327]
  We advance state-of-the-art in neural radiance fields (NeRFs) to enable physics-informed dense depth estimation and color correction. Our proposed method, WaterNeRF, estimates parameters of a physics-based model for underwater image formation. We can produce novel views of degraded as well as corrected underwater images, along with dense depth of the scene.
  arXiv  Detail & Related papers  (2022-09-27T00:53:26Z)
• Underwater Image Restoration via Contrastive Learning and a Real-world Dataset [59.35766392100753]
  We present a novel method for underwater image restoration based on unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images.
  arXiv  Detail & Related papers  (2021-06-20T16:06:26Z)
• Generating Physically-Consistent Satellite Imagery for Climate Visualizations [53.61991820941501]
  We train a generative adversarial network to create synthetic satellite imagery of future flooding and reforestation events. A pure deep learning-based model can generate flood visualizations but hallucinates floods at locations that were not susceptible to flooding. We publish our code and dataset for segmentation guided image-to-image translation in Earth observation.
  arXiv  Detail & Related papers  (2021-04-10T15:00:15Z)
• Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement [10.143025577499039]
  We propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images. A new method for simulating underwater-like training dataset from RGB-D data by underwater image formation model is proposed. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method.
  arXiv  Detail & Related papers  (2020-02-20T07:50:00Z)

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.