# UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data

Source: [UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data](<https://www.sota2.com/research/paper/68fa80801e209131cea9d4c2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T07:01:20.315Z

Authors: Ezequiel Perez-Zarate, Chunxiao Liu, Oscar Ramos-Soto, Diego Oliva, Marco Perez-Cisneros

Published: 2025-01-15T14:58:26.000Z

## Abstract

Restoring underwater images affected by non-uniform illumination (NUI) is essential to improve visual quality and usability in marine applications. Conventional methods often fall short in handling complex illumination patterns, while learning-based approaches face challenges due to the lack of targeted datasets. To address these limitations, the Underwater Non-uniform Illumination Restoration Network (UNIR-Net) is proposed. UNIR-Net integrates multiple components, including illumination enhancement, attention mechanisms, visual refinement, and contrast correction, to effectively restore underwater images affected by NUI. In addition, the Paired Underwater Non-uniform Illumination (PUNI) dataset is introduced, specifically designed for training and evaluating models under NUI conditions. Experimental results on PUNI and the large-scale real-world Non-Uniform Illumination Dataset (NUID) show that UNIR-Net achieves superior performance in both quantitative metrics and visual outcomes. UNIR-Net also improves downstream tasks such as underwater semantic segmentation, highlighting its practical relevance. The code of this method is available at https://github.com/xingyumex/UNIR-Net

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.09053>)

[PDF](<https://arxiv.org/pdf/2501.09053.pdf>)