UIF: An Objective Quality Assessment for Underwater Image Enhancement
| Autor: | Yannan Zheng, Weiling Chen, Rongfu Lin, Tiesong Zhao, Patrick Le Callet |
|---|---|
| Přispěvatelé: | Fuzhou University [Fuzhou], Image Perception Interaction (LS2N - équipe IPI), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ) |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV) [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Image and Video Processing (eess.IV) Image Quality Assessment (IQA) Computer Science - Computer Vision and Pattern Recognition FOS: Electrical engineering electronic engineering information engineering underwater image processing Underwater Image Enhancement (UIE) Electrical Engineering and Systems Science - Image and Video Processing Computer Graphics and Computer-Aided Design Software |
| Zdroj: | IEEE Transactions on Image Processing IEEE Transactions on Image Processing, 2022, 31, pp.5456-5468. ⟨10.1109/TIP.2022.3196815⟩ |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2022.3196815 |
| Popis: | Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performances with subjective and objective methods. However, the subjective evaluation is time-consuming and uneconomic for all images, while existing objective methods have limited capabilities for the newly-developed UIE approaches based on deep learning. To fill this gap, we propose an Underwater Image Fidelity (UIF) metric for objective evaluation of enhanced underwater images. By exploiting the statistical features of these images, we present to extract naturalness-related, sharpness-related, and structure-related features. Among them, the naturalness-related and sharpness-related features evaluate visual improvement of enhanced images; the structure-related feature indicates structural similarity between images before and after UIE. Then, we employ support vector regression to fuse the above three features into a final UIF metric. In addition, we have also established a large-scale UIE database with subjective scores, namely Underwater Image Enhancement Database (UIED), which is utilized as a benchmark to compare all objective metrics. Experimental results confirm that the proposed UIF outperforms a variety of underwater and general-purpose image quality metrics. Comment: This paper was submitted to ACMMM 2021 |
| Databáze: | OpenAIRE |
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