Proceedings ArticleDOI
Visibility enhancement for underwater visual SLAM based on underwater light scattering model
Younggun Cho,Ayoung Kim +1 more
- pp 710-717
TLDR
A real-time visibility enhancement algorithm for effective underwater visual simultaneous localization and mapping (SLAM) that starts with a thorough understanding of underwater particle physics and includes an artificial light model in the derivation.Abstract:
This paper presents a real-time visibility enhancement algorithm for effective underwater visual simultaneous localization and mapping (SLAM). Unlike an aerial environment, an underwater environment contains larger particles and is dominated by a different image degradation model. Our method starts with a thorough understanding of underwater particle physics (e.g., forward, back, multiple scattering, blur and noise). Targeting underwater image enhancement in a real-world application, we include an artificial light model in the derivation. The proposed method is effective for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method is validated by using simulated synthetic images (color) and real-world underwater images (color and grayscale). Using two underwater image sets acquired from the same area but with different water turbidity, we evaluate the proposed visibility enhancement and camera registration improvement in SLAM.read more
Citations
More filters
Journal ArticleDOI
An In-Depth Survey of Underwater Image Enhancement and Restoration
TL;DR: This exposition summarizes more than 120 studies about the latest progress in underwater image restoration and enhancement, including the techniques, datasets, available codes, and evaluation metrics, and provides detailed objective evaluations and analysis of the representative methods on five types of underwater scenarios, which verifies the applicability of these methods in different underwater conditions.
Journal ArticleDOI
Model-Assisted Multiband Fusion for Single Image Enhancement and Applications to Robot Vision
TL;DR: The main idea of the paper is combining model-based and fusion-based dehazing methods, thereby presenting balanced image enhancement while elaborating image details, and demonstrates outstanding performance on various types of hazy images.
Journal ArticleDOI
MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction
Xiaodong Liu,Zhi Gao,Ben M. Chen +2 more
TL;DR: In this work, a deep multiscale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction is proposed, resulting in better performance in both color correction and detail preservation.
Journal ArticleDOI
A Contrast-Guided Approach for the Enhancement of Low-Lighting Underwater Images
TL;DR: Experimental results and a comparison with other underwater-specific image enhancement methods show that the proposed framework can be used for significantly improving the visibility in low-lighting underwater images of different scales, without creating undesired dehazing artifacts.
Proceedings ArticleDOI
L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion
TL;DR: In this article, a single image low-light underwater image enhancer, L.............. 2.............. UWE, was proposed, which builds on the observation that an efficient model of atmospheric lighting can be derived from local contrast information.
References
More filters
Journal ArticleDOI
Guided Image Filtering
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: The guided filter is a novel explicit image filter derived from a local linear model that can be used as an edge-preserving smoothing operator like the popular bilateral filter, but it has better behaviors near edges.
Journal ArticleDOI
Single Image Haze Removal Using Dark Channel Prior
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: A simple but effective image prior - dark channel prior to remove haze from a single input image is proposed, based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel.
Book ChapterDOI
Guided image filtering
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: The guided filter is demonstrated that it is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling.
Journal ArticleDOI
DehazeNet: An End-to-End System for Single Image Haze Removal
TL;DR: DehazeNet as discussed by the authors adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing.
Journal ArticleDOI
Single image dehazing
TL;DR: Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.