Oceanic Radiance and Chromatic Adaptation: A Three-Stage Framework for Underwater Image Enhancement

A. Sumalatha; S. K. Aruna

doi:10.48084/etasr.17615

Authors

A. Sumalatha Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, Karnataka, India
S. K. Aruna Department of AI and Data Science Engineering, School of Engineering and Technology, Christ University, Bengaluru, Karnataka, India

Volume: 16 | Issue: 3 | Pages: 34758-34763 | June 2026 | https://doi.org/10.48084/etasr.17615

Received: 18 January 2026 | Revised: 6 March 2026 | Accepted: 14 March 2026 | Online: 17 April 2026
Corresponding author: A. Sumalatha

Abstract

Light absorption and backscattering in underwater environments cause severe chromatic aberration and visibility loss. To address this, we propose a modular framework for holistic restoration termed Oceanic Radiance and Chromatic Adaptation (ORCA). Contrary to conventional end-to-end deep learning architectures that behave like "black boxes," our proposed system divides the problem into three physically informed blocks. The Radiance Restoration module estimates back-scattering and transmission, the Chromatic Adaptation Network recovers the attenuated red channel, and the Adaptive Illumination block applies multi-scale contrast enhancement. By physically grounding these adaptive layers, ORCA preserves color constancy while emphasizing high frequency details. Benchmarking on U45 and UIEB datasets (N = 935 images) achieved a UIQM of 3.48 and MUSIQ of 72.7, outperforming current state-of-the-art methods. Statistical validation via Paired t-tests (p < 0.005) confirms ORCA as a reliable vision tool for autonomous underwater vehicles (AUVs).

Keywords:

autonomous underwater vehicles, chromatic adaptation, multi-scale fusion, oceanic radiance, perceived sharpness, spectral feature mapping, underwater image enhancement

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