Title: Underwater image enhancement using anisotropic diffusion and multiscale fusion strategy

Authors: Rekha Chaturvedi; Vishnu Soni; Jitendra Rajpurohit; Abhay Sharma

Addresses: Department of Data Science Engineering, School of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India ' Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, Rajasthan, India ' Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India ' Department of CSE (AI-ML), Adani University, Ahmedabad, Gujarat, India

Abstract: Since the transmission of light through water leads to scattering, consequently underwater images thus often afflicted by several types of degradation such as poor contrast, haziness, blurring, and colour distortions. In order to resolve these kinds of problems, we devise a novel technique that combines anisotropic diffusion to effectively split the LAB colour space's L-channel into base and detail images with the aim of reducing noise while simultaneously preserving the salient features of underwater images. The method further performs a fusion process to quantify three weight maps using a variety of strategies and yield normalised weight maps for each image. To achieve enhanced final image, we consolidate the blended contributions of all levels after appropriate upsampling. Lastly, we restore the enhanced underwater image by converting the blended enhanced LAB to RGB colour space image. Enhancement of image quality is measured in terms of Entropy, PCQL and UIQM. UIEB dataset has been used to implement our proposed method and experimental findings shows that our method outperforms the LAFFNet, deep residual, retinex based methods. It also works well for the underwater images having colour distortion, poor contrast and detail loss.

Keywords: underwater image enhancement; multiscale fusion; anisotropic diffusion; weight maps; Laplacian pyramid.

DOI: 10.1504/IJCVR.2026.155189

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.1, pp.1 - 18

Received: 09 Jun 2023
Accepted: 08 Feb 2024
Published online: 29 Jul 2026 *

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