Diving Deeper into Underwater Image Enhancement: A Survey (1907.07863v1)

Abstract: The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time, and hence; a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before. To be specific, we first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented including network architecture, network parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.

• The paper presents a comprehensive analysis of deep learning models like CNNs and GANs for underwater image enhancement.
• It evaluates diverse architectures and benchmarking techniques using metrics such as PSNR, SSIM, UCIQE, and UIQM on synthetic datasets.
• The survey highlights future directions including the need for real-world datasets and refined loss functions to improve model generalization.

Deep Learning in Underwater Image Enhancement: A Comprehensive Review

The paper “Diving Deeper into Underwater Image Enhancement: A Survey” by Saeed Anwar and Chongyi Li presents a thorough analysis of deep learning techniques in the field of underwater image enhancement. Given the complexities arising from selective absorption and scattering in underwater environments, the authors aim to bridge the gap between theoretical models and practical deep learning applications.

The survey begins by emphasizing the pivotal role of deep learning frameworks, such as CNNs and GANs, in addressing the challenges posed by degraded underwater imagery. These image distortions are mainly attributed to backscatter effects and selective light absorption, which result in low contrast, color deviation, and blurry details.

A note-worthy aspect of this paper is its dual focus:

1. Comprehensive Analysis: The authors provide a detailed survey of existing deep learning-based algorithms for underwater image enhancement. They categorize these methods into different models—encoder-decoder architectures, modular designs, multi-branch designs, depth-guided networks, and dual-generator GANs—highlighting the nuances and architectural differences among them.
2. Benchmarking Techniques: The paper emphasizes both qualitative and quantitative assessments of various algorithms on diverse datasets, filling a crucial gap as prior works seldom adopted a comparative benchmarking approach. Evaluation metrics and datasets specific to underwater imagery are discussed, including UIEBD, ULFID, and the Haze-line dataset.

Key Insights and Findings

The survey reveals a significant reliance on synthetic datasets due to the lack of comprehensive real-world underwater imagery with corresponding ground-truth references. These synthetic datasets are generated using underwater image formation models, although divergences from real-world conditions often lead to models lacking generalization capacity.

The paper meticulously describes a variety of evaluation metrics—PSNR, SSIM, UCIQE, and UIQM—used to quantify enhancement quality. It also presents a comparative analysis of methods including DUIENet, MCycleGAN, and UWCNN, elucidating their strengths and limitations.

Future Directions and Challenges

While the current methodologies demonstrate several advancements, the paper identifies areas for future exploration:

• Dataset Creation: Emphasizing the necessity for larger, more diverse real-world datasets to improve training efficacy and generalization abilities of models.
• Refinement of Loss Functions: Developing loss functions that account for underwater-specific degradation properties and integrate domain knowledge.
• Unsupervised Learning Methods: The potential of zero-shot and few-shot learning approaches is highlighted in the context of limited training data.

The paper ultimately serves as a comprehensive reference point for ongoing research in underwater image enhancement, calling for advancements in dataset development and more tailored deep learning architectures. By addressing existing challenges and emphasizing future research directions, the survey contributes significantly to the understanding and further development of deep learning-based underwater image enhancement technologies.