A Generalized Physical-knowledge-guided Dynamic Model for Underwater Image Enhancement

• URL: http://arxiv.org/abs/2308.05447v1
• Date: Thu, 10 Aug 2023 09:09:15 GMT
• Title: A Generalized Physical-knowledge-guided Dynamic Model for Underwater Image Enhancement
• Authors: Pan Mu, Hanning Xu, Zheyuan Liu, Zheng Wang, Sixian Chan, Cong Bai
• Abstract summary: We design a Generalized Underwater image enhancement method via a Physical-knowledge-guided Dynamic Model (short for GUPDM) This study changes the global atmosphere light and the transmission to simulate various underwater image types. We then design ADS and TDS that use dynamic convolutions to adaptively extract prior information from underwater images and generate parameters for PMS.
• Score: 15.625794673532509
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Underwater images often suffer from color distortion and low contrast resulting in various image types, due to the scattering and absorption of light by water. While it is difficult to obtain high-quality paired training samples with a generalized model. To tackle these challenges, we design a Generalized Underwater image enhancement method via a Physical-knowledge-guided Dynamic Model (short for GUPDM), consisting of three parts: Atmosphere-based Dynamic Structure (ADS), Transmission-guided Dynamic Structure (TDS), and Prior-based Multi-scale Structure (PMS). In particular, to cover complex underwater scenes, this study changes the global atmosphere light and the transmission to simulate various underwater image types (e.g., the underwater image color ranging from yellow to blue) through the formation model. We then design ADS and TDS that use dynamic convolutions to adaptively extract prior information from underwater images and generate parameters for PMS. These two modules enable the network to select appropriate parameters for various water types adaptively. Besides, the multi-scale feature extraction module in PMS uses convolution blocks with different kernel sizes and obtains weights for each feature map via channel attention block and fuses them to boost the receptive field of the network. The source code will be available at \href{https://github.com/shiningZZ/GUPDM}{https://github.com/shiningZZ/GUPDM}.

Related papers

• UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement [27.535028176427623]
  Underwater image enhancement (UIE) plays a crucial role in various marine applications. Current learning-based approaches frequently lack explicit prior knowledge about the physical processes involved in underwater image formation. This paper proposes a novel deep unfolding network (DUN) for UIE that integrates color priors and inter-stage feature incorporation.
  arXiv  Detail & Related papers  (2024-08-20T08:48:33Z)
• A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation [19.204227769408725]
  Existing underwater image enhancement approaches fail to accurately estimate imaging model parameters such as depth and veiling light. We propose a model-guided framework for jointly training a Deep Degradation Model with any advanced UIE model. Our framework achieves remarkable enhancement results across diverse underwater scenes.
  arXiv  Detail & Related papers  (2024-07-05T03:10:13Z)
• 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)
• Transmission and Color-guided Network for Underwater Image Enhancement [8.894719412298397]
  We propose an Adaptive Transmission and Dynamic Color guided network (named ATDCnet) for underwater image enhancement. To exploit the knowledge of physics, we design an Adaptive Transmission-directed Module (ATM) to better guide the network. To deal with the color deviation problem, we design a Dynamic Color-guided Module (DCM) to post-process the enhanced image color.
  arXiv  Detail & Related papers  (2023-08-09T11:43:54Z)
• 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)
• RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement [5.3240763486073055]
  We propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images. Our proposed conditional network is designed with Adaptive Fourier Gating Mechanism (AFGM) and Multiscale Conal Attention Module (MCAM) To more precisely correct the color cast and low saturation of the image, we introduce a Three-branch Feature Extraction (TFE) block in the primary net.
  arXiv  Detail & Related papers  (2023-02-23T17:27:05Z)
• CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training [112.96224800952724]
  We propose cascaded modulation GAN (CM-GAN) to generate plausible image structures when dealing with large holes in complex images. In each decoder block, global modulation is first applied to perform coarse semantic-aware synthesis structure, then spatial modulation is applied on the output of global modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios.
  arXiv  Detail & Related papers  (2022-03-22T16:13:27Z)
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding [88.46682991985907]
  We present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding.
  arXiv  Detail & Related papers  (2021-04-27T07:35:30Z)
• Conditional Variational Image Deraining [158.76814157115223]
  Conditional Variational Image Deraining (CVID) network for better deraining performance. We propose a spatial density estimation (SDE) module to estimate a rain density map for each image. Experiments on synthesized and real-world datasets show that the proposed CVID network achieves much better performance than previous deterministic methods on image deraining.
  arXiv  Detail & Related papers  (2020-04-23T11:51:38Z)

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.