Assessment of a Comprehensive Synthetic Underwater Image Dataset

Name: Assessment of a Comprehensive Synthetic Underwater Image Dataset
Creator: Kuruma Purnima
License: https://creativecommons.org/licenses/by/4.0/

Citation Author(s):: Purnima Kuruma (Research Scholar, Mohan Babu University)

C Siva Kumar (Associate Professor, Department of CSE, Mohan Babu University)
Submitted by:: Kuruma Purnima
Last updated:: Wed, 06/05/2024 - 01:53
DOI:: 10.21227/5ky3-gz67
Data Format:: table
Research Article Link:: Gradient-Based Design Metrics for Assessment of Underwater Image Enhancement
Links:: Non-Gradient Based Design Metrics for Underwater Image Enhancement

A Comprehensive Synthetic Underwater Image Dataset

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Categories:

Keywords:

focus quantification; underwater imaging; machine vision

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Abstract

The dataset contains the focus metrics values of a comprehensive synthetic underwater image dataset (https://data.mendeley.com/datasets/2mcwfc5dvs/1). The image dataset has 100 ground-truth images and 15,000 synthetic underwater images generated by considering a comprehensive set of effects of underwater environment. The current dataset focus on the focus metrics of these 15,100 images. The metrics considered are Absolute central moment (ACMO), Brenner's focus measure (BREN), Image curvature (CURV), Gray-level variance (GLVA), Gray-level local variance (GLLV), Gray-level variance normalized (GLVN), Squared gradient (GRAS), Helmli's measure (HELM), Histogram entropy (HISE), Histogram range (HISR), Energy of Laplacian (LAPE), Diagonal Laplacian (LAPD), Modified Laplacian (LAPM), Variance of Laplacian (LAPV), Tenengrad variance (TENV), Vollat's correlation (VOLA), Wavelet ratio (WAVR), Wavelet sum (WAVS), and Wavelet variance (WAVV). The literature categorizes these metrics as gradient-based and non-gradient-based.

Instructions:

Refer the following.

K. Purnima and C. S. Kumar, "Gradient-Based Design Metrics for Assessment of Underwater Image Enhancement," 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2023, pp. 783-788, doi: 10.1109/ICSSAS57918.2023.10331789.

K. Purnima and C. S. Kumar, "Non-Gradient Based Design Metrics for Underwater Image Enhancement," 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2023, pp. 817-823, doi: 10.1109/ICSSAS57918.2023.10331864.

Kuruma Purnima, C Siva Kumar, “A Comprehensive Synthetic Underwater Image Dataset,” Mendeley Data, V1, 2024. doi: 10.17632/2mcwfc5dvs.1

The dataset enables a chance of better analysis of underwater image enhancement tasks.

Kuruma Purnima Wed, 06/05/2024 - 01:58 Permalink