Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method

• URL: http://arxiv.org/abs/2404.07649v1
• Date: Thu, 11 Apr 2024 11:12:06 GMT
• Title: Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method
• Authors: Tashmoy Ghosh,
• Abstract summary: We have utilized the cycle consistent learning technique of the state-of-the-art Cycle GAN model with modification in the loss function. We trained the Cycle GAN model with the modified loss functions on the benchmarked Enhancing Underwater Visual Perception dataset. The upgraded images provide better results from conventional models and further for under water navigation, pose estimation, saliency prediction, object detection and tracking.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we have present an improved Cycle GAN based model for under water image enhancement. We have utilized the cycle consistent learning technique of the state-of-the-art Cycle GAN model with modification in the loss function in terms of depth-oriented attention which enhance the contrast of the overall image, keeping global content, color, local texture, and style information intact. We trained the Cycle GAN model with the modified loss functions on the benchmarked Enhancing Underwater Visual Perception (EUPV) dataset a large dataset including paired and unpaired sets of underwater images (poor and good quality) taken with seven distinct cameras in a range of visibility situation during research on ocean exploration and human-robot cooperation. In addition, we perform qualitative and quantitative evaluation which supports the given technique applied and provided a better contrast enhancement model of underwater imagery. More significantly, the upgraded images provide better results from conventional models and further for under water navigation, pose estimation, saliency prediction, object detection and tracking. The results validate the appropriateness of the model for autonomous underwater vehicles (AUV) in visual navigation.

Related papers

• FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation [65.01601309903971]
  We introduce FAFA, a Frequency-Aware Flow-Aided self-supervised framework for 6D pose estimation of unmanned underwater vehicles (UUVs) Our framework relies solely on the 3D model and RGB images, alleviating the need for any real pose annotations or other-modality data like depths. We evaluate the effectiveness of FAFA on common underwater object pose benchmarks and showcase significant performance improvements compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-25T03:54:01Z)
• Improved Image-based Pose Regressor Models for Underwater Environments [0.0]
  We regress a 6-degree-of-freedom pose from single RGB images with high accuracy. We explore data augmentation with stereo camera images to improve model accuracy. Experimental results demonstrate that the models achieve high accuracy in both simulated and clear waters.
  arXiv  Detail & Related papers  (2024-03-13T09:20:43Z)
• MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility [1.9272863690919875]
  We introduce MuLA-GAN, a novel approach that leverages the synergistic power of Geneversarative Adrial Networks (GANs) and Multi-Level Attention mechanisms for comprehensive underwater image enhancement. Our model excels in capturing and preserving intricate details in underwater imagery, essential for various applications. This work not only addresses a significant research gap in underwater image enhancement but also underscores the pivotal role of Multi-Level Attention in enhancing GANs.
  arXiv  Detail & Related papers  (2023-12-25T07:33:47Z)
• 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)
• WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond [52.27796682972484]
  Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. We propose a normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Considering the differentiability and interpretability, we incorporate the prior into the data-driven mapping procedure.
  arXiv  Detail & Related papers  (2023-08-02T04:17:35Z)
• 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)
• 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.