UIR-Net: A Simple and Effective Baseline for Underwater Image Restoration and Enhancement
Abstract
:1. Introduction
- 1.
- In this paper, we present a novel, end-to-end, lightweight underwater image restoration network named UIR-Net, which can generate context-rich and spatially accurate outputs.
- 2.
- UIR-Net is the first network that combines underwater image enhancement and underwater image restoration, which means this can be considered a pioneering study with great significance for practical applications and deployments.
- 3.
- This article proposes the Marine Spot Impurity Removal Benchmarking (MSIRB) dataset and UIEBD-Snow.
- 4.
2. Related Works
2.1. Underwater Image Restoration
2.2. Underwater Image Enhancement
3. Method
3.1. Channel Residual Prior
3.2. Gradient Strategy
3.3. Loss Function
4. Experimental
4.1. Datasets of Underwater Restoration
- 1.
- Marine Snow Removal Benchmarking Dataset (MSRB)In this section, we present the specifications of the ocean snow artifacts synthesized by the MSRB dataset [17], which has been mainly built for two general tasks of marine snow artifacts removal: MSR task 1 is dedicated removing small-size artifacts, while MSR task 2 is used to cope with various artifacts of different sizes.Obviously, it would be much more difficult to handle multiple sizes of underwater snow than just focusing on small-sized ones. Corresponding to each MSR task, each sub-dataset is composed of a training set with 2300 pairs of images and a test set with 400 pairs, all with a pixel resolution of 384 × 384. Each image pair includes an original underwater collected picture and one containing synthetic marine snow artifacts, original underwater collected picture is the ground truth of the dataset. Each composite image is added with N marine snow particles, while N, which is the number of added particles, is generated by a discrete uniform distribution of U{100,600}. Based on our preliminary observations, in each synthetic image, H-type and V-type ocean snow spots are randomly generated with a probability of 0.7 for H-type and 0.3 for V-type. Most of our parameters are chosen based on our observations of real-world collected images and the corresponding artificial influence of ocean snow.
- 2.
- Marine Spot Impurity Removal Benchmarking Dataset (MSIRB)At present, there is no accessible underwater dataset of ocean snow images that can be used to train and test deep neural networks to eliminate ocean snow particles, which represents a major inconvenience to relevant ongoing research on marine light spot removal algorithms. In order to present the diverse morphological features of the real-world ocean snow particles and the complex composition of ocean snow scenes, we propose a new dataset called the Marine Spot Impurity Removal Benchmarking Dataset (MSIRB). The MSIRB dataset contains (1) synthetic ocean light spot images, (2) the corresponding real images and (3) an ocean light spot mask. We reference [47] and use its mask to produce underwater light spot images, each basic mask corresponds to small, medium, and large grain sizes. The dataset is shown in Figure 4.It is important to focus on the fact that inside the MSIRB dataset’s ground truth and input binary images, there is no difference in color, only the presence or absence of light spot impurities.
- 3.
- An underwater image enhancement benchmark dataset and beyond with Snow (UIEBD-Snow)To explore the generalization of our work in image enhancement tasks, the same operation that was applied on the MSIRB dataset is performed in this paper on the UIEBD dataset, a dataset with its potential ground truth generated by adopting a dozen of cutting-edge image improvement methods. Various morphological features of real snow particles of the ocean are fused inside the UIEBD to make our new dataset called UIEBD-Snow, which contains 890 underwater images with corresponding reference maps.It is worth noting that the difference between UIEBD-Snow and MSIRB is mainly in the color correction, and the dataset example can be referred to Figure 4
4.2. Training Parameter Settings
4.3. Evaluation Metrics
- 1.
- Full-Reference EvaluationWe adopt the standard metrics (PSNR, SSIM and RMSE [48]) as full-reference evaluation.PSNR is short for Peak Signal to Noise Ratio. The better the PSNR, the less the image distortion. We can obtain the PSNR as follows:Assume that the current image I and the reference images K, H and W are the height and width of the image respectively. MSE can be calculated as follows:SSIM is short for Structural Similarity, we can obtain the SSIM of the two images (image x and image y) as follows:RMSE is short for Root Mean Square Error. We can obtain RMSE as follows:
- 2.
- Non-Reference EvaluationUIR-Net is supervised network, there is an image pair for input and the corresponding target, but we also adopt the UCIQE [49] and UIQM [50] as non-reference evaluation, which are commonly used for underwater image quality assessment.UCIQE is a linear combination of color intensity, saturation and contrast, used to quantitatively evaluate the non-homogeneous color shift, blurring and low contrast of underwater images. The better the UCIQE, the better the underwater image quality, we can obtain UCIQE as follows:UIQM is short for underwater image quality measurement. We can obtain it as follows:
4.4. Comparison with State-of-the-Art Methods on Underwater Image Restoration
4.5. Comparison with State-of-the-Art Methods on Underwater Image Enhancement
4.6. Qualitative Evaluations
4.6.1. Quantitative Comparisons of Underwater Image Restoration on the MSRB Dataset
4.6.2. Quantitative Comparisons of Underwater Restoration on MSIRB Dataset
4.6.3. Quantitative Comparisons of Underwater Enhancement on UIEBD-Snow Dataset
4.6.4. FLOPs and Params Comparisons with MPRNet and DGUNet
4.7. Ablation Study
4.7.1. Ablation Study of Channel Prior and Gradient Descent
4.7.2. Ablation Study of
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Method | PSNR | SSIM | RMSE | UIQM | UICQE |
---|---|---|---|---|---|
Nature image restoration | |||||
AirNet [51] | 24.359 | 0.954 | 6.745 | 3.418 | 0.549 |
DB-ResNet [26] | 23.258 | 0.920 | 9.060 | 3.531 | 0.553 |
PReNet [27] | 21.837 | 0.899 | 13.932 | 3.722 | 0.524 |
Maxim [28] | 24.578 | 0.931 | 7.836 | 3.606 | 0.553 |
Restormer [29] | 24.411 | 0.954 | 6.783 | 3.977 | 0.550 |
DGUNet [31] | 33.292 | 0.977 | 3.408 | 3.755 | 0.567 |
MPRNet [30] | 35.322 | 0.984 | 2.659 | 3.715 | 0.566 |
Our | 34.464 | 0.984 | 2.649 | 3.877 | 0.576 |
Method | PSNR | SSIM | RMSE | UIQM | UICQE |
---|---|---|---|---|---|
Nature image restoration | |||||
AirNet [51] | 20.536 | 0.922 | 9.878 | 3.761 | 0.575 |
DB-ResNet [26] | 20.506 | 0.897 | 10.743 | 3.758 | 0.574 |
PReNet [27] | 20.310 | 0.888 | 14.237 | 4.075 | 0.550 |
Maxim [28] | 23.599 | 0.924 | 8.316 | 3.832 | 0.565 |
Restormer [29] | 20.396 | 0.927 | 9.817 | 3.810 | 0.574 |
DGUNet [31] | 33.785 | 0.984 | 2.584 | 3.911 | 0.562 |
MPRNet [30] | 33.561 | 0.985 | 2.592 | 3.725 | 0.554 |
Our | 34.513 | 0.986 | 2.391 | 3.837 | 0.565 |
Method | PSNR | SSIM | RMSE | UIQM | UICQE |
---|---|---|---|---|---|
Underwater image enhancement | |||||
PUIE [52] | 16.926 | 0.723 | 18.030 | 3.594 | 0.606 |
CWR [53] | 15.374 | 0.556 | 23.276 | 4.620 | 0.507 |
IMF [54] | 17.006 | 0.707 | 18.904 | 3.354 | 0.633 |
FUnIE-GAN [55] | 15.619 | 0.486 | 24.762 | 5.106 | 0.596 |
Nature image restoration | |||||
MPRNet [30] | 20.027 | 0.775 | 15.048 | 3.400 | 0.598 |
DGUNet [31] | 20.711 | 0.795 | 14.068 | 3.363 | 0.597 |
Our | 21.200 | 0.807 | 13.142 | 3.610 | 0.596 |
Method | FLOPs | Total Params | Training Time |
---|---|---|---|
DGUNet [31] | 12,176,119 | 94,600 s | |
MPRNet [30] | 3,637,249 | 41,400 s | |
UIR-Net | 3,632,740 | 22,000 s |
residual prior | proximal gradient descent | PSNR | SSIM | RMSE |
33.319 | 0.981 | 2.953 | ||
√ | 33.319 | 0.981 | 2.953 | |
√ | 34.356 | 0.981 | 2.931 | |
√ | √ | 34.464 | 0.984 | 2.649 |
residual prior | proximal gradient descent | PSNR | SSIM | RMSE |
32.477 | 0.982 | 2.912 | ||
√ | 33.295 | 0.983 | 2.649 | |
√ | 34.121 | 0.984 | 2.765 | |
√ | √ | 34.513 | 0.986 | 2.391 |
Paramater | PSNR | SSIM | RMSE |
---|---|---|---|
= 0.1 | 20.203 | 0.781 | 14.030 |
= 0.05 | 19.371 | 0.791 | 13.301 |
= 0.02 | 20.039 | 0.797 | 13.904 |
= 0.01 | 21.200 | 0.807 | 13.142 |
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Mei, X.; Ye, X.; Zhang, X.; Liu, Y.; Wang, J.; Hou, J.; Wang, X. UIR-Net: A Simple and Effective Baseline for Underwater Image Restoration and Enhancement. Remote Sens. 2023, 15, 39. https://doi.org/10.3390/rs15010039
Mei X, Ye X, Zhang X, Liu Y, Wang J, Hou J, Wang X. UIR-Net: A Simple and Effective Baseline for Underwater Image Restoration and Enhancement. Remote Sensing. 2023; 15(1):39. https://doi.org/10.3390/rs15010039
Chicago/Turabian StyleMei, Xinkui, Xiufen Ye, Xiaofeng Zhang, Yusong Liu, Junting Wang, Jun Hou, and Xuli Wang. 2023. "UIR-Net: A Simple and Effective Baseline for Underwater Image Restoration and Enhancement" Remote Sensing 15, no. 1: 39. https://doi.org/10.3390/rs15010039