Digital Image Processing

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Noise Removal Algorithm for Underwater Images using Adaptive Fusion of Bilateral and Curvelet Transform


Affiliations
1 Department of Compter Science, College of Computer Science & Information Technology, JAZAN University, Saudi Arabia
2 Department of Computer Engineering & Networks College of CS & IT, Jazan University, Saudi Arabia
3 Department of ECE, RVS Technical campus Coimbatore, Coimbatore, India
     

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Images captured under water are noisy due to various reasons. The traditional filters and novel filtering algorithms found in the literature lead to failure in preserving small scale details such as curves and textures in noise images. This paper proposes a noise removal algorithm for underwater images that uses the significant features of bilateral filter, CT (Curvelet Transform) and fusion mechanism in increasing the performance of denoised image.


Keywords

Noise Removal, Curvelet, Bilateral Filter, Edge Preserve, Texture Preserve.
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  • Noise Removal Algorithm for Underwater Images using Adaptive Fusion of Bilateral and Curvelet Transform

Abstract Views: 232  |  PDF Views: 1

Authors

M. Shanmugasundaram
Department of Compter Science, College of Computer Science & Information Technology, JAZAN University, Saudi Arabia
Mousa Khubrani
Department of Compter Science, College of Computer Science & Information Technology, JAZAN University, Saudi Arabia
Prakash Kuppuswamy
Department of Computer Engineering & Networks College of CS & IT, Jazan University, Saudi Arabia
N. Shanmuga Vadivu
Department of ECE, RVS Technical campus Coimbatore, Coimbatore, India

Abstract


Images captured under water are noisy due to various reasons. The traditional filters and novel filtering algorithms found in the literature lead to failure in preserving small scale details such as curves and textures in noise images. This paper proposes a noise removal algorithm for underwater images that uses the significant features of bilateral filter, CT (Curvelet Transform) and fusion mechanism in increasing the performance of denoised image.


Keywords


Noise Removal, Curvelet, Bilateral Filter, Edge Preserve, Texture Preserve.