Fusion-based underwater image enhancement by wavelet decomposition

Wang, Yafei; Ding, Xueyan; Wang, Ruo‐Qian; Zhang, Jun; Fu, Xianping

doi:10.1109/icit.2017.7915500

Cited by 37 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blind likelihood detection means that the receiver processes signal under the condition that the mathematical model and statistical parameters of the channel are not known, then it can complete the accurate estimation of the channel and the detection of the signal. Compared with the traditional MMSE (Minimum Mean Square Error) algorithm [12] 、 MD (Mean Detection) algorithm [13][14] and blind detection algorithm, the fast likelihood blind detection algorithm has lower complexity and higher accuracy. When the channel conditions such as channel model and parameters are unknown, the fast likelihood blind detection algorithm can complete the signal detection and channel model estimation by processing the received signal.…”

Section: Fast-likelihood Blind Detection Algorithmmentioning

confidence: 99%

Underwater image transmission system based on visible light communication

Zhou¹,

Wang²,

Guan³

et al. 2021

Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, Peop

View full text Add to dashboard Cite

In order to improve the image transmission quality of underwater visible light communication, this paper designs an optimal scheme of the underwater image transmission system based on blue visible light communication. In the hardware circuit module, the trans-impedance amplifier circuit of the T-feedback network is used to improve the signal receiving performance. Due to the bad feature of the underwater channel, a fast likelihood blind detecting algorithm was adopted to detect, collect and process the data in this article. The method proposed in the paper is able to reduce BER (Bit Error Rate) as well as speed up computation. It can be seen from the experimental results that the scheme of the system optimization effectively decreases DC bias of the photoelectric receiver, as well as decreases the computation complexity and improves the transmitting efficiency under the condition of meeting the requirements for the specific BER of the receiving signal.

show abstract

Section: Fast-likelihood Blind Detection Algorithmmentioning

confidence: 99%

Underwater image transmission system based on visible light communication

Zhou¹,

Wang²,

Guan³

et al. 2021

Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, Peop

View full text Add to dashboard Cite

show abstract

“…Wavelet decomposition has been widely used in image processing in various fields: biometric identification, compression, classification, image retrieval, image Watermarking [15][16][17][18][19][20][21][22][23] and has many advantages over Fourier transform. Wavelet transform is a well localized in both the time and frequency domain.…”

Section: Wavelet and Gall Wavelet 41 Wavelet Decompositionmentioning

confidence: 99%

Reducing image search time by improved BOVW with wavelet decomposition

Amin¹,

Beladgham²,

Taleb‐Ahmed³

2020

IJEECS

View full text Add to dashboard Cite

<p>In the last decade, the bag of visual words (BOVW) has been used widely in image classification, image retrieval and has significantly improved the performance of CBIR system. In this paper we propose a new method to enhance BOVW using features obtained from wavelet decomposition in order to reduce computational costs in vocabulary construction and training time. We apply several level of wavelet decompositions and evaluate their impact on accuracy of the BOVW. We apply our method on MURA-v1.1 dataset and the experiments results confirm the performance of our approach.</p>

show abstract

“…It performs some filtering processing on the transformed coefficients in a certain transformation domain of the image, and then inversely transforms them to the spatial domain to achieve the enhanced image. According to the characteristics of underwater images, in recent years, scholars have proposed many typical underwater image enhancement methods, for example, based on the quaternion attenuation coefficient inversion recovery processing algorithm [53], integration, RGB and HSI color model enhancement algorithm [54], color correction method based on the ACE model [55] point spread function (PSF), processing algorithm [56], underwater image based on wavelength compensation to “atomization” enhancement methods [57], etc. Some other methods mainly improve the underwater image enhancement algorithm, such as the underwater image enhancement method based on RETINEX [58].…”

Section: Related Work In Tactile Theory and Underwater Image Restomentioning

confidence: 99%

Development of Tactile Imaging for Underwater Structural Damage Detection

Chen

Hou

et al. 2019

Sensors

View full text Add to dashboard Cite

Underwater structural damage inspection has mainly relied on diver-based visual inspection, and emerging technologies include the use of remotely operated vehicles (ROVs) for improved efficiency. With the goal of performing an autonomous and robotic underwater inspection, a novel Tactile Imaging System for Underwater Inspection (TISUE) is designed, prototyped, and tested in this paper. The system has two major components, including the imaging subsystem and the manipulation subsystem. The novelty lies in the imaging subsystem, which consists of an elastomer-enabled contact-based optical sensor with specifically designed artificial lighting. The completed TISUE system, including optical imaging, data storage, display analytics, and a mechanical support subsystem, is further tested in a laboratory experiment. The experiment demonstrates that high-resolution and high-quality images of structural surface damage can be obtained using tactile ‘touch-and-sense’ imaging, even in a turbid water environment. A deep learning-based damage detection framework is developed and trained. The detection results demonstrate the similar detectability of five damage types in the obtained tactile images to images obtained from regular (land-based) structural inspection.

show abstract

Fusion-based underwater image enhancement by wavelet decomposition

Cited by 37 publications

References 13 publications

Underwater image transmission system based on visible light communication

Underwater image transmission system based on visible light communication

Reducing image search time by improved BOVW with wavelet decomposition

Development of Tactile Imaging for Underwater Structural Damage Detection

Contact Info

Product

Resources

About