Underwater Image Restoration through Color Correction and UW-Net
Abstract
:1. Introduction
- Development of UW-Net, a neural network model leveraging DWT and IDWT for enhanced feature extraction, tailored specifically for underwater image restoration tasks.
- Implementation of a color correction method that effectively addresses color loss in the red and blue channels, utilizing the Gray World Hypothesis to significantly enhance the visual quality of the restored images.
- Integration of a chromatic adaptation layer within UW-Net, which significantly improves the contrast and color fidelity of the output images, resulting in a noticeable enhancement in the overall image quality.
2. Related Work
Quality Evaluation in Image Super-Resolution
3. Proposed Method
3.1. Color Correction
3.2. Wavelet Transform
3.3. UW-Net Model
3.4. Image Restoration
4. Results and Discussions
4.1. Experimental Setup
4.2. Evaluation Metrics
4.3. Comparative Analysis
4.4. Ablation Study
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Non-Learning Methods | Learning-Based Methods | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Metrics | Images | HDP | IBLA | HLP | TEBCF | WWPF | PCDE | PUIE | SNet | SCEIR | Ours |
RMSE/SSIM | test_p120_ | 0.10/0.75 | 0.12/0.80 | 0.18/0.70 | 0.12/0.75 | 0.12/0.77 | 0.26/0.36 | 0.10/0.96 | 0.10/0.85 | 0.09/0.76 | 0.07/0.81 |
test_p14_ | 0.16/0.55 | 0.09/0.84 | 0.12/0.73 | 0.12/0.76 | 0.13/0.77 | 0.23/0.45 | 0.13/0.77 | 0.11/0.71 | 0.20/0.50 | 0.09/0.73 | |
test_p164_ | 0.11/0.63 | 0.08/0.78 | 0.19/0.50 | 0.14/0.55 | 0.13/0.53 | 0.28/0.25 | 0.12/0.72 | 0.12/0.71 | 0.12/0.45 | 0.10/0.62 | |
test_p286_ | 0.09/0.80 | 0.07/0.77 | 0.13/0.65 | 0.09/0.78 | 0.11/0.77 | 0.22/0.34 | 0.14/0.91 | 0.09/0.75 | 0.12/0.78 | 0.06/0.81 | |
test_p339_ | 0.14/0.63 | 0.14/0.61 | 0.16/0.68 | 0.13/0.75 | 0.17/0.76 | 0.26/0.34 | 0.16/0.67 | 0.11/0.61 | 0.10/0.76 | 0.10/0.60 | |
test_p386_ | 0.26/0.40 | 0.16/0.58 | 0.09/0.72 | 0.08/0.84 | 0.09/0.79 | 0.19/0.36 | 0.13/0.82 | 0.10/0.65 | 0.10/0.83 | 0.10/0.43 | |
test_p474_ | 0.10/0.77 | 0.12/0.72 | 0.18/0.68 | 0.12/0.80 | 0.11/0.75 | 0.27/0.39 | 0.11/0.92 | 0.10/0.82 | 0.07/0.84 | 0.08/0.76 | |
test_p528_ | 0.18/0.60 | 0.18/0.48 | 0.24/0.46 | 0.14/0.59 | 0.08/0.74 | 0.32/0.22 | 0.10/0.82 | 0.11/0.67 | 0.10/0.62 | 0.07/0.76 | |
PSNR/NIQE | test_p120_ | 19.83/6.32 | 18.76/5.08 | 15.02/5.78 | 18.49/6.63 | 18.32/6.55 | 11.73/5.76 | 20.10/4.00 | 19.90/7.35 | 21.20/6.11 | 23.20/6.05 |
test_p14_ | 16.09/4.24 | 20.49/3.53 | 18.54/4.06 | 18.76/4.34 | 17.94/3.32 | 12.62/3.24 | 17.60/3.52 | 19.09/3.71 | 13.91/4.44 | 20.70/3.78 | |
test_p164_ | 19.21/4.99 | 22.04/4.80 | 14.47/5.13 | 17.34/5.61 | 17.51/5.01 | 11.16/5.20 | 18.70/3.51 | 18.74/5.43 | 18.24/4.92 | 20.40/4.95 | |
test_p286_ | 20.72/4.59 | 22.52/6.16 | 17.95/5.87 | 20.92/4.70 | 19.07/4.49 | 13.24/6.03 | 16.80/4.94 | 20.70/4.47 | 18.55/4.63 | 24.60/5.18 | |
test_p339_ | 16.92/6.09 | 17.36/6.28 | 15.74/5.19 | 18.05/5.84 | 15.27/5.99 | 11.64/6.41 | 15.90/4.51 | 18.96/6.62 | 19.72/5.56 | 20.10/5.57 | |
test_p386_ | 11.60/3.48 | 16.08/4.21 | 21.13/5.32 | 21.71/4.70 | 20.95/4.21 | 14.28/4.37 | 17.40/4.98 | 19.72/3.92 | 19.99/4.16 | 20.10/4.76 | |
test_p474_ | 19.94/5.28 | 18.28/5.20 | 15.09/5.03 | 18.58/4.68 | 19.05/5.33 | 11.41/5.13 | 18.80/4.07 | 19.68/5.48 | 23.26/4.93 | 22.30/6.01 | |
test_p528_ | 15.11/4.39 | 14.81/5.29 | 12.45/4.94 | 16.92/4.91 | 21.52/4.92 | 10.02/5.79 | 20.30/4.02 | 19.52/5.26 | 20.39/4.82 | 23.20/5.14 |
Non-Learning Methods | Learning-Based Methods | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Metrics | Images | HDP | IBLA | HLP | TEBCF | WWPF | PCDE | PUIE | SNet | SCEIR | Ours |
RMSE/SSIM | 44 | 0.30/0.27 | 0.29/0.32 | 0.30/0.27 | 0.11/0.66 | 0.10/0.81 | 0.34/0.32 | 0.07/0.73 | 0.06/0.90 | 0.29/0.50 | 0.09/0.70 |
811 | 0.23/0.54 | 0.24/0.43 | 0.23/0.54 | 0.11/0.71 | 0.06/0.82 | 0.34/0.26 | 0.07/0.78 | 0.09/0.78 | 0.14/0.74 | 0.10/0.75 | |
1617 | 0.19/0.67 | 0.06/0.87 | 0.19/0.67 | 0.13/0.84 | 0.08/0.89 | 0.20/0.42 | 0.07/0.71 | 0.02/0.97 | 0.14/0.66 | 0.06/0.75 | |
1730 | 0.11/0.84 | 0.09/0.87 | 0.11/0.84 | 0.14/0.86 | 0.12/0.87 | 0.30/0.51 | 0.13/0.80 | 0.08/0.90 | 0.26/0.57 | 0.13/0.68 | |
2016 | 0.15/0.39 | 0.22/0.31 | 0.15/0.39 | 0.15/0.69 | 0.14/0.71 | 0.21/0.28 | 0.14/0.80 | 0.09/0.72 | 0.11/0.66 | 0.07/0.62 | |
2856 | 0.10/0.70 | 0.09/0.79 | 0.10/0.70 | 0.08/0.84 | 0.05/0.80 | 0.19/0.30 | 0.13/0.67 | 0.09/0.84 | 0.09/0.59 | 0.05/0.72 | |
3218 | 0.25/0.38 | 0.09/0.63 | 0.25/0.38 | 0.13/0.62 | 0.07/0.76 | 0.33/0.21 | 0.06/0.77 | 0.09/0.71 | 0.14/0.67 | 0.05/0.73 | |
4013 | 0.16/0.63 | 0.09/0.72 | 0.16/0.63 | 0.12/0.80 | 0.11/0.77 | 0.23/0.37 | 0.10/0.65 | 0.06/0.81 | 0.11/0.71 | 0.07/0.72 | |
PSNR/NIQE | 44 | 10.34/3.21 | 10.77/3.23 | 10.30/3.21 | 18.97/3.24 | 20.20/3.21 | 9.40/4.22 | 23.19/2.81 | 25.16/3.06 | 10.86/2.72 | 20.90/3.54 |
811 | 12.84/3.64 | 12.42/3.47 | 12.80/3.64 | 18.95/5.13 | 25.00/3.37 | 9.36/3.58 | 23.05/3.92 | 20.65/3.58 | 17.12/4.37 | 19.80/5.04 | |
1617 | 14.57/4.82 | 24.66/4.57 | 14.60/4.82 | 18.05/4.16 | 22.00/4.18 | 13.90/4.21 | 23.55/4.54 | 33.06/4.55 | 17.24/4.32 | 24.50/6.00 | |
1730 | 18.81/3.29 | 20.63/3.26 | 18.80/3.29 | 17.20/3.44 | 18.10/3.27 | 10.40/4.68 | 17.80/3.46 | 21.70/3.29 | 11.64/3.55 | 17.90/4.94 | |
2016 | 16.76/4.29 | 13.02/4.10 | 16.80/4.29 | 16.59/4.37 | 17.10/4.11 | 13.60/6.70 | 17.28/4.81 | 20.73/4.05 | 18.85/4.67 | 23.40/4.43 | |
2856 | 19.92/4.54 | 20.79/4.45 | 19.90/4.54 | 21.80/4.09 | 25.40/4.53 | 14.20/4.36 | 17.48/4.34 | 21.29/4.84 | 21.32/3.62 | 25.90/4.23 | |
3218 | 12.18/3.89 | 20.52/3.96 | 12.20/3.89 | 18.01/4.04 | 23.60/3.73 | 9.70/4.86 | 24.27/4.08 | 20.95/3.98 | 17.23/4.40 | 26.30/4.83 | |
4013 | 16.16/3.92 | 20.50/4.09 | 16.20/3.92 | 18.39/4.37 | 19.00/3.97 | 12.80/4.21 | 19.69/3.96 | 24.01/3.88 | 19.44/4.40 | 23.20/5.01 |
Non-Learning Methods | Learning-Based Methods | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Dataset | Metrics | HDP | IBLA | HLP | TEBCF | WWPF | PCDE | PUIE | SNet | SCEIR | Ours |
EUVP | RMSE | 0.19 | 0.16 | 0.15 | 0.14 | 0.16 | 0.26 | 0.11 | 0.14 | 0.12 | 0.11 |
PSNR | 15.10 | 17.32 | 17.01 | 17.40 | 16.30 | 12.00 | 19.70 | 17.70 | 18.60 | 19.90 | |
SSIM | 0.47 | 0.62 | 0.60 | 0.63 | 0.63 | 0.32 | 0.56 | 0.60 | 0.68 | 0.55 | |
NIQE | 5.04 | 5.00 | 5.00 | 5.11 | 5.17 | 6.34 | 5.49 | 5.18 | 4.93 | 5.02 | |
LSUI | RMSE | 0.17 | 0.17 | 0.16 | 0.13 | 0.13 | 0.26 | 0.12 | 0.13 | 0.10 | 0.11 |
PSNR | 16.10 | 16.50 | 16.54 | 17.90 | 18.10 | 11.80 | 19.10 | 18.10 | 20.70 | 19.87 | |
SSIM | 0.54 | 0.56 | 0.58 | 0.67 | 0.71 | 0.31 | 0.65 | 0.66 | 0.75 | 0.64 | |
NIQE | 4.31 | 4.16 | 4.20 | 4.45 | 4.48 | 5.58 | 4.22 | 4.34 | 4.14 | 4.75 |
Color Correction (CC) | Chromatic Layer (CL) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Datasets | W/ CC | W/O CC | RMSE | SSIM | PSNR | NIQE | W/ CL | W/O CL | RMSE | SSIM | PSNR | NIQE |
EUVP | ✓ | × | 0.11 | 0.55 | 19.89 | 5.02 | ✓ | × | 0.11 | 0.55 | 19.89 | 5.02 |
× | ✓ | 0.12 | 0.64 | 18.87 | 5.20 | × | ✓ | 0.17 | 0.58 | 15.54 | 5.01 | |
LSUI | ✓ | × | 0.11 | 0.64 | 19.87 | 4.75 | ✓ | × | 0.11 | 0.64 | 19.87 | 4.75 |
× | ✓ | 0.14 | 0.50 | 17.51 | 7.99 | × | ✓ | 0.23 | 0.23 | 13.10 | 6.27 |
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Awan, H.S.A.; Mahmood, M.T. Underwater Image Restoration through Color Correction and UW-Net. Electronics 2024, 13, 199. https://doi.org/10.3390/electronics13010199
Awan HSA, Mahmood MT. Underwater Image Restoration through Color Correction and UW-Net. Electronics. 2024; 13(1):199. https://doi.org/10.3390/electronics13010199
Chicago/Turabian StyleAwan, Hafiz Shakeel Ahmad, and Muhammad Tariq Mahmood. 2024. "Underwater Image Restoration through Color Correction and UW-Net" Electronics 13, no. 1: 199. https://doi.org/10.3390/electronics13010199
APA StyleAwan, H. S. A., & Mahmood, M. T. (2024). Underwater Image Restoration through Color Correction and UW-Net. Electronics, 13(1), 199. https://doi.org/10.3390/electronics13010199