BlueRealm

BlueRealm Underwater Image Dataset Dataset Metadata Name: BlueRealm Description: A comprehensive dataset designed for training and evaluating underwater image enhancement and restoration methods. Purpose: Provide a diverse collection of underwater imagery for developing and testing algorithms related to underwater image restoration. Dataset Composition: Combined Dataset Size: 6,000 images (after augmentation).Original Dataset Size: 3,010 images. 2,102 underwater images from a GoPro camera. Datasets from (Akkaynak & Treibitz, 2019) and (Li et al., 2019). Video frames captured by a mini-ROV camera. Image Characteristics: Diverse underwater environments, depths, and lighting conditions. Includes images with a color chart for accurate quality assessment. Video frames from an ROV camera for evaluating real-time processing. Data Augmentation: The original dataset was augmented to 6,000 images using random rotations and flips. Reference Data: Images corrected using SeaThru method (Akkaynak & Treibitz, 2019) are provided as reference data for training and evaluation. Data Acquisition: GoPro camera. Mini-ROV camera. Publicly available datasets from previous studies. Preprocessing: SeaThru method applied to generate reference corrected images. Augmentation via rotations and flips. Availability: The proprietary dataset (GoPro and mini ROV images) is publicly available at this link: BlueRealm dataset Citation: The BlueRealm dataset has been published in the context of the following work: C. Antoniou, S. Spanos, S. Vellas, V. Ntouskos, K. Karantzalos, (2024). StreamUR: Physics-informed Near Real-Time Underwater Image Restoration. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.DOI: 10.5194/isprs-archives-XLVIII-3-2024-1-2024 Also cite the original datasets from (Akkaynak & Treibitz, 2019) and (Li et al., 2019), if applicable. Contact: Christos Antoniou - chrant.mail at gmail.com Sotiris Spanos - spanossotiris1998 at gmail.com Valsamis Ntouskos - valsamis.ntouskos at unimercatorum.it; ntouskos at mail.ntua.gr References: Akkaynak, Derya, and Tali Treibitz. "Sea-thru: A method for removing water from underwater images." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019. Li, Chongyi, et al. "An underwater image enhancement benchmark dataset and beyond." IEEE transactions on image processing 29 (2019): 4376-4389.