Colour Balancing with Average Filtering and Principle Component Analysis for Underwater Images/Video Enhancement
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Rahul Khoondl, Bhawna Goyal, Ayush Dogra
|DOI : 10.14445/22315381/IJETT-V69I2P224|
MLA Style: Rahul Khoondl, Bhawna Goyal, Ayush Dogra "Colour Balancing with Average Filtering and Principle Component Analysis for Underwater Images/Video Enhancement" International Journal of Engineering Trends and Technology 69.2(2021):171-177.
APA Style:Rahul Khoondl, Bhawna Goyal, Ayush Dogra. Colour Balancing with Average Filtering and Principle Component Analysis for Underwater Images/Video Enhancement. International Journal of Engineering Trends and Technology, 69(2), 171-177.
Underwater video and imagery play a vital role in collecting and analyzing underwater information. Quality images and videos can provide accurate information for underwater systems. Quality images and videos can provide accurate information for underwater systems. Unfortunately, underwater images are responsible for low resolution, color shades, blurring, low-light exposure, and irregular illumination, which severely impacts the detection and analysis of underwater data. In order to enhance images obtained underwater, there are various methods have been developed. In this paper, we propose a new underwater image enhancement model that consists of four steps, namely: color correction, YUV channel decomposition, improving Y channel, and creation of the final image. The first step involves the color correction of the original image. In the second step, this color image is transformed into YUV space, and the luminance component is refined. The third step makes use of average filtering and histogram equalization along with principal component analysis. Finally, underwater images are obtained by transforming YUV color space into RGB color space. To enhance underwater videos, source data are decomposed into frames that are enhanced using the proposed methodology. The experimental analysis indicates that the proposed model outperforms other state-of-art methods
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Underwater video/image enhancement, color balancing, color channel decomposition, average filtering (AvgFil.), histogram equalization (Heq.), principle component analysis (PCA).