A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
- URL: http://arxiv.org/abs/2407.04230v1
- Date: Fri, 5 Jul 2024 03:10:13 GMT
- Title: A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
- Authors: Dazhao Du, Enhan Li, Lingyu Si, Fanjiang Xu, Jianwei Niu, Fuchun Sun,
- Abstract summary: 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.
- Score: 19.204227769408725
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Due to the selective absorption and scattering of light by diverse aquatic media, underwater images usually suffer from various visual degradations. Existing underwater image enhancement (UIE) approaches that combine underwater physical imaging models with neural networks often fail to accurately estimate imaging model parameters such as depth and veiling light, resulting in poor performance in certain scenarios. To address this issue, we propose a physical model-guided framework for jointly training a Deep Degradation Model (DDM) with any advanced UIE model. DDM includes three well-designed sub-networks to accurately estimate various imaging parameters: a veiling light estimation sub-network, a factors estimation sub-network, and a depth estimation sub-network. Based on the estimated parameters and the underwater physical imaging model, we impose physical constraints on the enhancement process by modeling the relationship between underwater images and desired clean images, i.e., outputs of the UIE model. Moreover, while our framework is compatible with any UIE model, we design a simple yet effective fully convolutional UIE model, termed UIEConv. UIEConv utilizes both global and local features for image enhancement through a dual-branch structure. UIEConv trained within our framework achieves remarkable enhancement results across diverse underwater scenes. Furthermore, as a byproduct of UIE, the trained depth estimation sub-network enables accurate underwater scene depth estimation. Extensive experiments conducted in various real underwater imaging scenarios, including deep-sea environments with artificial light sources, validate the effectiveness of our framework and the UIEConv model.
Related papers
- UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation [5.596432047035205]
Underwater monocular depth estimation serves as the foundation for tasks such as 3D reconstruction of underwater scenes.
Existing methods fail to consider the unique characteristics of underwater environments.
In this paper, an end-to-end learning framework for underwater monocular depth estimation called UMono is presented.
arXiv Detail & Related papers (2024-07-25T07:52:11Z) - ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation [62.600382533322325]
We propose a novel monocular depth estimation method called ScaleDepth.
Our method decomposes metric depth into scene scale and relative depth, and predicts them through a semantic-aware scale prediction module.
Our method achieves metric depth estimation for both indoor and outdoor scenes in a unified framework.
arXiv Detail & Related papers (2024-07-11T05:11:56Z) - WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation [4.909989222186828]
We propose WaterMono, a novel framework for depth estimation and image enhancement.
It incorporates the following key measures: (1) We present a Teacher-Guided Anomaly Mask to identify dynamic regions within the images; (2) We employ depth information combined with the Underwater Image Formation Model to generate enhanced images, which in turn contribute to the depth estimation task; and (3) We utilize a rotated distillation strategy to enhance the model's rotational robustness.
arXiv Detail & Related papers (2024-06-19T08:49:45Z) - 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) - 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) - Fully Self-Supervised Depth Estimation from Defocus Clue [79.63579768496159]
We propose a self-supervised framework that estimates depth purely from a sparse focal stack.
We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions.
arXiv Detail & Related papers (2023-03-19T19:59:48Z) - Semantic-aware Texture-Structure Feature Collaboration for Underwater
Image Enhancement [58.075720488942125]
Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics.
We develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model.
We also apply the proposed algorithm to the underwater salient object detection task to reveal the favorable semantic-aware ability for high-level vision tasks.
arXiv Detail & Related papers (2022-11-19T07:50:34Z) - Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection
Consistency [114.02182755620784]
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision.
Our framework is shown to outperform the state-of-the-art depth and motion estimation methods.
arXiv Detail & Related papers (2021-02-04T14:26:42Z) - Perceptual underwater image enhancement with deep learning and physical
priors [35.37760003463292]
We propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor.
Due to the lack of training data, a hybrid underwater image synthesis model, which fuses physical priors and data-driven cues, is proposed to synthesize training data.
Experimental results show the superiority of our proposed method over several state-of-the-art methods on both real-world and synthetic underwater datasets.
arXiv Detail & Related papers (2020-08-21T22:11:34Z) - 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.