NDLI: SE–RWNN: an synergistic evolution and randomly wired neural network-based model for adaptive underwater image enhancement

Content Provider	IET Digital Library
Author	Li, Yang Chen, Rong
Abstract	Under water images are likely to suffer from severe degradation such as colour distortion, low contrast, and fuzz content, caused by the absorption and scattering effects of the water. To improve the visual appearance of the image, the authors present an adaptive algorithm for effective underwater image enhancement using a randomly wired neural network (RWNN) and synergistic evolution (SE). In doing so, they sequentially conduct colours adjustment, contrast improvement and luminance enhancement while enhancing details by an edge-preserving technique. To set up the system, they develop a multi-strategy cooperating evolution algorithm to figure out the optimal parameter values. Extensive experimental results show that the proposed model improves both subjectively and quantitatively the quality of underwater images.
Starting Page	4349
Ending Page	4358
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 16, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1677
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/16
Journal	IET Image Processing
Publisher Date	2020-12-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Absorption Adaptive Algorithm Adaptive Underwater Image Enhancement Colour Distortion Colours Adjustment Computer Vision And Image Processing Technique Contrast Improvement Edge Detection Edge-preserving Technique Effective Underwater Image Enhancement Fuzz Content Geophysical Image Processing Geophysics Computing Image Colour Analysis Image Denoising Image Enhancement Luminance Enhancement Multistrategy Cooperating Evolution Algorithm Neural Nets Neural Network-based Model Oceanographic And Hydrological Technique And Equipment Oceanographic Technique Optical, Image And Video Signal Processing Randomly Wired Neural Network Scattering Effect Severe Degradation SE–RWNN Synergistic Evolution Underwater Image Visual Appearance Water Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

UCT-GAN: underwater image colour transfer generative adversarial network

Hue preserving-based approach for underwater colour image enhancement

Improvement of underwater colour correction using standard deviation ratio

Deep pixel-to-pixel network for underwater image enhancement and restoration

Nighttime image enhancement using a new illumination boost algorithm

Review of underwater image restoration algorithms

Local brightness adaptive image colour enhancement with Wasserstein distance

Demosaicking algorithm for white-RGB CFA images

Combining highlight removal and low-light image enhancement technique for HDR-like image generation

SE–RWNN: an synergistic evolution and randomly wired neural network-based model for adaptive underwater image enhancement

Similar Documents

UCT-GAN: underwater image colour transfer generative adversarial network

Hue preserving-based approach for underwater colour image enhancement

Improvement of underwater colour correction using standard deviation ratio

Deep pixel-to-pixel network for underwater image enhancement and restoration

Nighttime image enhancement using a new illumination boost algorithm

Review of underwater image restoration algorithms

Local brightness adaptive image colour enhancement with Wasserstein distance

Demosaicking algorithm for white-RGB CFA images

Combining highlight removal and low-light image enhancement technique for HDR-like image generation

SE–RWNN: an synergistic evolution and randomly wired neural network-based model for adaptive underwater image enhancement