Underwater image enhancement with Image Colorfulness Measure

• URL: http://arxiv.org/abs/2004.08609v1
• Date: Sat, 18 Apr 2020 12:44:57 GMT
• Title: Underwater image enhancement with Image Colorfulness Measure
• Authors: Hui Li, Xi Yang, ZhenMing Li, TianLun Zhang
• Abstract summary: We propose a novel enhancement model, which is a trainable end-to-end neural model. For better details, contrast and colorfulness, this enhancement network is jointly optimized by the pixel-level and characteristiclevel training criteria.
• Score: 7.292965806774365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfulness, this enhancement network is jointly optimized by the pixel-level and characteristiclevel training criteria. Through extensive experiments on natural underwater scenes, we show that the proposed method can get high quality enhancement results.

Related papers

• Underwater Image Enhancement with Cascaded Contrastive Learning [51.897854142606725]
  Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectively address the diverse degradations simultaneously. We propose a two-stage deep learning framework and taking advantage of cascaded contrastive learning to guide the network training of each stage.
  arXiv  Detail & Related papers  (2024-11-16T03:16:44Z)
• Underwater Image Enhancement via Dehazing and Color Restoration [17.263563715287045]
  Existing underwater image enhancement methods treat the haze and color cast as a unified degradation process. We propose a Vision Transformer (ViT)-based network (referred to as WaterFormer) to improve the underwater image quality.
  arXiv  Detail & Related papers  (2024-09-15T15:58:20Z)
• Dual High-Order Total Variation Model for Underwater Image Restoration [13.789310785350484]
  Underwater image enhancement and restoration (UIER) is one crucial mode to improve the visual quality of underwater images. We propose an effective variational framework based on an extended underwater image formation model (UIFM) In our proposed framework, the weight factors-based color compensation is combined with the color balance to compensate for the attenuated color channels and remove the color cast.
  arXiv  Detail & Related papers  (2024-07-20T13:06:37Z)
• 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)
• Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions [0.0]
  Images acquired during underwater activities suffer from environmental properties of the water, such as turbidity and light attenuation. Recent works related to underwater image enhancement, and based on deep learning approaches, tackle the lack of paired datasets generating synthetic ground-truth. We present a self-supervised learning methodology for underwater image enhancement based on deep learning that requires no paired datasets.
  arXiv  Detail & Related papers  (2022-08-04T19:55:40Z)
• 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)
• Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement [52.49231695707198]
  We investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Inspired by the color image formulation, we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. Our proposed method has surpassed the SOTA by 0.95dB in PSNR on LOL1000 dataset and 3.18% in mAP on ExDark dataset.
  arXiv  Detail & Related papers  (2021-03-19T04:00:27Z)
• Underwater Image Color Correction by Complementary Adaptation [0.0]
  We propose a novel approach for underwater image color correction based on a Tikhonov type optimization model in the CIELAB color space. Understood as a long-term adaptive process, our method effectively removes the underwater color cast and yields a balanced color distribution.
  arXiv  Detail & Related papers  (2020-10-21T03:59:22Z)
• 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.