A Comprehensive Survey on Image Dehazing Based on Deep Learning
- URL: http://arxiv.org/abs/2106.03323v1
- Date: Mon, 7 Jun 2021 03:51:25 GMT
- Title: A Comprehensive Survey on Image Dehazing Based on Deep Learning
- Authors: Jie Gui, Xiaofeng Cong, Yuan Cao, Wenqi Ren, Jun Zhang, Jing Zhang,
Dacheng Tao
- Abstract summary: The presence of haze significantly reduces the quality of images.
Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images.
There are few studies that summarize the deep learning (DL) based dehazing technologies.
- Score: 89.77554550654227
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The presence of haze significantly reduces the quality of images. Researchers
have designed a variety of algorithms for image dehazing (ID) to restore the
quality of hazy images. However, there are few studies that summarize the deep
learning (DL) based dehazing technologies. In this paper, we conduct a
comprehensive survey on the recent proposed dehazing methods. Firstly, we
summarize the commonly used datasets, loss functions and evaluation metrics.
Secondly, we group the existing researches of ID into two major categories:
supervised ID and unsupervised ID. The core ideas of various influential
dehazing models are introduced. Finally, the open issues for future research on
ID are pointed out.
Related papers
- A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
Underwater image enhancement (UIE) presents a significant challenge within computer vision research.
Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
arXiv Detail & Related papers (2024-05-30T04:46:40Z) - InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images [11.916941756499435]
In this paper, we explore the intricate task of incremental few-shot object detection in remote sensing images.
We introduce a pioneering fine-tuning-based technique, termed InfRS, designed to facilitate the incremental learning of novel classes.
We develop a prototypical calibration strategy based on the Wasserstein distance to mitigate the catastrophic forgetting problem.
arXiv Detail & Related papers (2024-05-18T13:39:50Z) - Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches [4.516330345599765]
High-quality images are crucial in remote sensing and UAV applications.
atmospheric haze can severely degrade image quality, making image dehazing a critical research area.
arXiv Detail & Related papers (2024-05-13T07:35:24Z) - Guided Depth Map Super-resolution: A Survey [88.54731860957804]
Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image.
A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques.
This survey is an effort to present a comprehensive survey of recent progress in GDSR.
arXiv Detail & Related papers (2023-02-19T15:43:54Z) - IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [88.35145788575348]
Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing.
The lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
We construct a comprehensive image anomaly detection benchmark (IM-IAD), which includes 19 algorithms on seven major datasets.
arXiv Detail & Related papers (2023-01-31T01:24:45Z) - Deep Image Deblurring: A Survey [165.32391279761006]
Deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image.
Recent advances in deep learning have led to significant progress in solving this problem.
arXiv Detail & Related papers (2022-01-26T01:31:30Z) - Fine-Grained Image Analysis with Deep Learning: A Survey [146.22351342315233]
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition.
This paper attempts to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval.
arXiv Detail & Related papers (2021-11-11T09:43:56Z)
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.