LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement
- URL: http://arxiv.org/abs/2406.14973v1
- Date: Fri, 21 Jun 2024 08:33:13 GMT
- Title: LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement
- Authors: Haodong Yang, Jisheng Xu, Zhiliang Lin, Jianping He,
- Abstract summary: Lightweight Underwater Unet (LU2Net) is a novel U-shape network designed specifically for real-time enhancement of underwater images.
LU2Net is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method.
- Score: 4.353142366661057
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement.
Related papers
- UnDIVE: Generalized Underwater Video Enhancement Using Generative Priors [9.438388237767105]
We propose a two-stage framework for enhancing underwater videos.
The first stage uses a denoising diffusion descriptive model to learn a generative prior from unlabeled data.
In the second stage, this prior is incorporated into a physics-based image formulation for spatial enhancement.
Our method enables real-time and computationally-efficient processing of high-resolution underwater videos at lower resolutions.
arXiv Detail & Related papers (2024-11-08T11:16:36Z) - Aquatic-GS: A Hybrid 3D Representation for Underwater Scenes [6.549998173302729]
We propose Aquatic-GS, a hybrid 3D representation approach for underwater scenes that effectively represents both the objects and the water medium.
Specifically, we construct a Neural Water Field (NWF) to implicitly model the water parameters, while extending the latest 3D Gaussian Splatting (3DGS) to model the objects explicitly.
Both components are integrated through a physics-based underwater image formation model to represent complex underwater scenes.
arXiv Detail & Related papers (2024-10-31T22:24:56Z) - Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions [48.529493393948435]
The visible-light camera has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems.
The visual imaging quality inevitably suffers from several kinds of degradations under complex weather conditions.
We develop a general-purpose multi-scene visibility enhancement method to restore degraded images captured under different weather conditions.
arXiv Detail & Related papers (2024-09-02T23:46:27Z) - Dual Adversarial Resilience for Collaborating Robust Underwater Image
Enhancement and Perception [54.672052775549]
In this work, we introduce a collaborative adversarial resilience network, dubbed CARNet, for underwater image enhancement and subsequent detection tasks.
We propose a synchronized attack training strategy with both visual-driven and perception-driven attacks enabling the network to discern and remove various types of attacks.
Experiments demonstrate that the proposed method outputs visually appealing enhancement images and perform averagely 6.71% higher detection mAP than state-of-the-art methods.
arXiv Detail & Related papers (2023-09-03T06:52:05Z) - 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) - Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN [60.257791714663725]
We propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes.
The proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.
arXiv Detail & Related papers (2022-12-23T03:00:28Z) - 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) - Medium Transmission Map Matters for Learning to Restore Real-World
Underwater Images [3.0980025155565376]
We introduce the media transmission map as guidance to assist in image enhancement.
The proposed method can achieve advanced results of 22.6 dB on the challenging Test-R90 with an impressive 30 times faster than the existing models.
arXiv Detail & Related papers (2022-03-17T16:13:52Z) - 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) - LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater
Image Enhancement [6.338178373376447]
We propose a lightweight adaptive feature fusion network (LAFFNet) for underwater image enhancement.
Our method reduces the number of parameters from 2.5M to 0.15M but outperforms state-of-the-art algorithms by extensive experiments.
arXiv Detail & Related papers (2021-05-04T05:31:10Z) - 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.