A deep learning approach for marine snow synthesis and removal
- URL: http://arxiv.org/abs/2311.15584v1
- Date: Mon, 27 Nov 2023 07:19:41 GMT
- Title: A deep learning approach for marine snow synthesis and removal
- Authors: Fernando Galetto and Guang Deng
- Abstract summary: This paper proposes a novel method to reduce the marine snow interference using deep learning techniques.
We first synthesize realistic marine snow samples by training a Generative Adversarial Network (GAN) model.
We then train a U-Net model to perform marine snow removal as an image to image translation task.
- Score: 55.86191108738564
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Marine snow, the floating particles in underwater images, severely degrades
the visibility and performance of human and machine vision systems. This paper
proposes a novel method to reduce the marine snow interference using deep
learning techniques. We first synthesize realistic marine snow samples by
training a Generative Adversarial Network (GAN) model and combine them with
natural underwater images to create a paired dataset. We then train a U-Net
model to perform marine snow removal as an image to image translation task. Our
experiments show that the U-Net model can effectively remove both synthetic and
natural marine snow with high accuracy, outperforming state-of-the-art methods
such as the Median filter and its adaptive variant. We also demonstrate the
robustness of our method by testing it on the MSRB dataset, which contains
synthetic artifacts that our model has not seen during training. Our method is
a practical and efficient solution for enhancing underwater images affected by
marine snow.
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) - An Efficient Detection and Control System for Underwater Docking using
Machine Learning and Realistic Simulation: A Comprehensive Approach [5.039813366558306]
This work compares different deep-learning architectures to perform underwater docking detection and classification.
A Generative Adversarial Network (GAN) is used to do image-to-image translation, converting the Gazebo simulation image into an underwater-looking image.
Results show an improvement of 20% in the high turbidity scenarios regardless of the underwater currents.
arXiv Detail & Related papers (2023-11-02T18:10:20Z) - Efficient-3DiM: Learning a Generalizable Single-image Novel-view
Synthesizer in One Day [63.96075838322437]
We propose a framework to learn a single-image novel-view synthesizer.
Our framework is able to reduce the total training time from 10 days to less than 1 day.
arXiv Detail & Related papers (2023-10-04T17:57:07Z) - 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) - MetaUE: Model-based Meta-learning for Underwater Image Enhancement [25.174894007563374]
This paper proposes a model-based deep learning method for restoring clean images under various underwater scenarios.
The meta-learning strategy is used to obtain a pre-trained model on the synthetic underwater dataset.
The model is then fine-tuned on real underwater datasets to obtain a reliable underwater image enhancement model, called MetaUE.
arXiv Detail & Related papers (2023-03-12T02:38:50Z) - Adaptive Uncertainty Distribution in Deep Learning for Unsupervised
Underwater Image Enhancement [1.9249287163937976]
One of the main challenges in deep learning-based underwater image enhancement is the limited availability of high-quality training data.
We propose a novel unsupervised underwater image enhancement framework that employs a conditional variational autoencoder (cVAE) to train a deep learning model.
We show that our proposed framework yields competitive performance compared to other state-of-the-art approaches in quantitative as well as qualitative metrics.
arXiv Detail & Related papers (2022-12-18T01:07:20Z) - Unsupervised Restoration of Weather-affected Images using Deep Gaussian
Process-based CycleGAN [92.15895515035795]
We describe an approach for supervising deep networks that are based on CycleGAN.
We introduce new losses for training CycleGAN that lead to more effective training, resulting in high-quality reconstructions.
We demonstrate that the proposed method can be effectively applied to different restoration tasks like de-raining, de-hazing and de-snowing.
arXiv Detail & Related papers (2022-04-23T01:30:47Z) - Marine Snow Removal Benchmarking Dataset [9.117162374919715]
This paper introduces a new benchmarking dataset for marine snow removal of underwater images.
We mathematically model two typical types of marine snow from the observations of real underwater images.
We propose two marine snow removal tasks using the dataset and show the first benchmarking results of marine snow removal.
arXiv Detail & Related papers (2021-03-26T03:54:43Z) - Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement [78.58603635621591]
Training an unpaired synthetic-to-real translation network in image space is severely under-constrained.
We propose a semi-supervised approach that operates on the disentangled shading and albedo layers of the image.
Our two-stage pipeline first learns to predict accurate shading in a supervised fashion using physically-based renderings as targets.
arXiv Detail & Related papers (2020-03-27T21:45:41Z) - 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.