Abstract
The images that are captured underwater contain various artifacts and also degraded due to the inherent properties of the location parameters such as dispersion and refraction. In general, most of the fusion algorithms in image processing applications are mostly based on fusion of two images, but in this work, it is only one image approach, where the same image is converted into varied forms of two images based on color balance and white balance procedures. The proposed method indicates that images are enhanced by means of global contrast and also preserving the edges of the images with little modifications. Basic filtering operations are also utilized in this work to enhance the images in order to retain the integrity of the images to identify the underwater objects vividly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schettini R, Corchs S (2010) Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J Adv Signal Processing 2010
Singh N, Ahuja NJ (2019) Implementation and evaluation of intelligence incorporated tutoring system. Int J Innov Technol Explor Eng (IJITEE) 8:4548–4558
Lu H, Li Y, Zhang Y et al (2017) Underwater optical image processing: a comprehensive review. Mobile Net Appl 22:1204–1211
Koschmieder H (1924) Theorie der horizontalen sichtweite, Beitrage zur Physik der freien Atmosphare, pp 33–53
Singh N, Kumar A, Ahuja NJ (2019) Implementation and evaluation of personalized intelligent tutoring system. Int J Innov Technol Explor Eng (IJITEE) 8:46–55
Kocak MD, Dalgleish FR, Caimi MF, Schechner YY (2008) A focus on recent developments and trends in underwater imaging. Marine Technol Soc J 42(1):52–67
Prasad PS, Devi BS, Reddy MJ, Gunjan VK (2018) A survey of fingerprint recognition systems and their applications. In: Proceedings of the international conference on communications and cyber physical engineering 2018, Hyderabad, India, 24–25 Jan 2018, pp 513–520
Angeline R, Kanna SN, Menon NG, Ashwath B (2022) Identifying malignancy of lung cancer using deep learning concepts. In: Garg L, Basterrech S, Banerjee C, Sharma TK (eds) Artificial intelligence in healthcare. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-16-6265-2_3
Mazel CH (2006) In situ measurement of reflectance and fluorescence spectra to support hyperspectral remote sensing and marine biology research. In: Proceedings of the OCEANS 2006, Boston, MA, USA, 18–21 Sept 2006; pp 1–4
Foresti G (2001) Visual inspection of sea bottom structures by an autonomous underwater vehicle. IEEE Trans Syst Man Cybern Part B (Cybernetics) 31:691–705
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chaitanya, K.G., Chandana, B., Devi, S.J., Gowthami, P., Reddy, B.V. (2023). Underwater Image Enhancement Using Color Balance and Image Fusion via Gamma Correction. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2021. Lecture Notes in Electrical Engineering, vol 947. Springer, Singapore. https://doi.org/10.1007/978-981-19-5936-3_72
Download citation
DOI: https://doi.org/10.1007/978-981-19-5936-3_72
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5935-6
Online ISBN: 978-981-19-5936-3
eBook Packages: Computer ScienceComputer Science (R0)