A Correction-Based Dynamic Enhancement Framework towards Underwater Detection

• URL: http://arxiv.org/abs/2302.02553v1
• Date: Mon, 6 Feb 2023 04:11:52 GMT
• Title: A Correction-Based Dynamic Enhancement Framework towards Underwater Detection
• Authors: Yanling Qiu, Qianxue Feng, Boqin Cai, Hongan Wei, and Weiling Chen
• Abstract summary: We propose a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance.
• Score: 5.103759034134962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance.

Related papers

• 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)
• Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios [41.34564703212461]
  We propose a bilevel optimization formulation for jointly learning underwater object detection and image enhancement. Our method outputs visually favoring images and higher detection accuracy.
  arXiv  Detail & Related papers  (2023-07-07T11:54:06Z)
• 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)
• Controllable Image Enhancement [66.18525728881711]
  We present a semiautomatic image enhancement algorithm that can generate high-quality images with multiple styles by controlling a few parameters. An encoder-decoder framework encodes the retouching skills into latent codes and decodes them into the parameters of image signal processing functions.
  arXiv  Detail & Related papers  (2022-06-16T23:54:53Z)
• The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement [50.29722732653095]
  There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality. We propose a loop enhancement framework that produces a clear picture of how the enhancement of low-light images could be optimized towards better visual quality.
  arXiv  Detail & Related papers  (2022-02-20T06:20:06Z)
• Adaptive Remote Sensing Image Attribute Learning for Active Object Detection [43.029857143916345]
  This paper takes adaptive brightness adjustment and scale adjustment as examples, and proposes an active object detection method based on deep reinforcement learning. The goal of adaptive image attribute learning is to maximize the detection performance.
  arXiv  Detail & Related papers  (2021-01-16T11:37:50Z)
• A Generative Approach for Detection-driven Underwater Image Enhancement [19.957923413999673]
  We present a model that integrates generative adversarial network (GAN)-based image enhancement with diver detection task. Our proposed approach restructures the GAN objective function to include information from a pre-trained diver detector. We train our network on a large dataset of scuba divers, using a state-of-the-art diver detector, and demonstrate its utility on images collected from oceanic explorations.
  arXiv  Detail & Related papers  (2020-12-10T21:33:12Z)
• 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)
• A Benchmark dataset for both underwater image enhancement and underwater object detection [34.25890702670983]
  We provide a large-scale underwater object detection dataset with both bounding box annotations and high quality reference images. The OUC dataset provides a platform to comprehensive study the influence of underwater image enhancement algorithms on the underwater object detection task.
  arXiv  Detail & Related papers  (2020-06-29T03:12:50Z)

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.