An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging (1907.03246v1)

Abstract: Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://github.com/wangyanckxx/Single-Underwater-Image-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

• The paper experimentally evaluates and categorizes IFM-free and IFM-based methods, highlighting trade-offs in addressing underwater image degradation.
• It employs comprehensive subjective and objective analyses to reveal challenges like over-enhancement and inaccurate parameter estimation.
• The study emphasizes the need for benchmark datasets and refined evaluation metrics to advance underwater image processing research.

Overview of Image Enhancement and Restoration for Underwater Imaging

This paper, published in the IEEE Access Multidisciplinary Journal, presents a detailed experimental-based review of image enhancement and restoration methods specifically targeted at improving underwater imagery. The authors, Yan Wang, Wei Song, Giancarlo Fortino, Lizhe Qi, Wenqiang Zhang, and Antonio Liotta, have methodically addressed the challenges associated with underwater image degradation, primarily caused by light absorption and scattering. Underwater images are essential for numerous applications in ocean exploration, but their quality is often compromised due to environmental factors unique to underwater conditions.

Key Contributions

The paper categorizes quality improvement methods into two primary classes: IFM-free image enhancement methods and IFM-based image restoration methods. The categorization is based on whether the methods leverage the optical imaging physical model, known as the underwater image formation model (IFM), or operate independently from it.

1. Review of Image Formation and Degradation: The authors introduce the underwater image formation model to explain the causes of image quality reduction, such as attenuation of red wavelengths causing a green-bluish color cast, low contrast, and increased haze.
2. Comprehensive Evaluation: This review includes an experimental comparison of state-of-the-art image enhancement and restoration techniques, utilizing both subjective and objective analysis. The provided code aims to ensure replicability and further exploration in this domain.
3. Critical Appraisal: The paper critiques existing methods in terms of image restoration challenges, particularly concerning prior-based parameter estimation for IFM-based approaches. Additionally, it shares insights into the inherent difficulties of enhancing underwater image quality.

Numerical Results and Implications

The review highlights significant issues with both IFM-free and IFM-based methods. IFM-free techniques often lead to over-enhancement or unnatural colors due to the reliance on pixel intensity redistribution without physical degradation consideration. Conversely, IFM-based restoration approaches suffer in areas with inaccurate background light (BL) and transmission map (TM) estimations. The authors underscore the necessity for a publicly available benchmark dataset of underwater imagery to aid in the advancement of these methodologies.

Comparative experimental results showcase the limitations of current assessment methods, accentuating the need for more sophisticated evaluation tools tailored to underwater images. The paper also suggests future directions, advocating for the integration of high-level computer vision tasks with low-level enhancement processes to maximize application efficacy.

Future Directions

Given the complexity of underwater environments and the demand for robust image processing techniques, several avenues for future research and development are suggested:

• Dataset Development: Establishment of comprehensive benchmark datasets that include varied underwater conditions is vital for improving deep learning model training and validation.
• Enhanced Evaluation Metrics: The paper calls for refined image quality assessment metrics that accurately reflect underwater image conditions.
• Interdisciplinary Approach: Combining low-level image enhancement with high-level computer vision tasks could yield better visual quality and feature extraction capabilities.
• Technological Scalability: Development of computationally efficient methods that adapt to diverse underwater scenes remains a pivotal goal.

Conclusion

The paper provides a thorough examination of underwater image enhancement and restoration techniques, shedding light on the current state of the art and paving the way for future improvements. Through experimental evaluations and theoretical insights, it extends support to researchers striving to enhance underwater imagery—a critical component in advancing ocean exploration technologies.