Underwater Image Restoration Method Research Articles

Underwater image restoration via spatially adaptive polarization imaging and color correction

Polarization imaging is extensively employed in underwater image restoration owing to its effectiveness in eliminating backscattered light. However, the existing polarization-based imaging methods rely on the strong assumption that the degrees of polarization (DoPs) of the target and backscattered light remain constant. Additionally, these methods ignore wavelength-specific absorption phenomena. To address these challenges, we propose an underwater image restoration method via spatially adaptive polarization imaging and color correction. First, based on optimal polarization difference images, we propose a method for calculating the spatial DoP of the target. In this method, the polarization characteristics of the target and background are fully exploited, and a scoring function is designed to achieve an optimal differential image. Second, we propose an adaptive local optimization method to estimate the spatial DoP of backscattering, which involves dividing the image into multiple local regions, and then employing improved particle swarm optimization (IPSO) algorithms to obtain the optimal value of DoP for each region. Finally, we introduce an adaptive compensation method based on the channel with minimal color absorption to correct color shifts during underwater polarization imaging. Extensive experiments demonstrate that our method exhibits superior generalization capability and outstanding restoration performance compared to existing methods.

Underwater image restoration method based on Walsh–Hadamard transform and attenuation coefficient estimation

Abstract Underwater images often exhibit color distortion and low contrast due to the scattering and absorption of light as it travels through water. Changes in lighting conditions further complicate the restoration and enhancement of these images. Improving the quality of underwater images is crucial for advancements in fields such as marine biology research, underwater measurement, and environmental monitoring. This paper proposes an underwater image restoration method based on the Image Formation Model (IFM), utilizing the Walsh–Hadamard transform and attenuation coefficient estimation. Traditional methods rely on dark channel prior and maximum intensity prior to estimate background light (BL) and transmission maps (TMs), often performing poorly in various underwater environments. Our method uses image blur to estimate BL and depth maps and derives three-channel attenuation coefficients using the gray-world theory to obtain a more accurate TM. Experimental results on real underwater images show that our method effectively eliminates color deviation and contrast distortion while preserving image details, significantly outperforming other IFM-based restoration techniques. Compared to the closest competing algorithms, our method achieves better UIQM and UCIQE scores.

Underwater image restoration based on dual information modulation network

The presence of light absorption and scattering in underwater conditions results in underwater images with missing details, low contrast, and color bias. The current deep learning-based methods bring unlimited potential for underwater image restoration (UIR) tasks. These methods, however, do not adequately take into account the inconsistency of the attenuation of different color channels and spatial regions when performing image restoration. To solve these gaps, we propose a dual information modulation network (DIMN) for accurate UIR tasks. To be specific, we design a multi-information enhancement module (MIEM), empowered by spatial-aware attention block (SAAB) and multi-scale structural Transformer block (MSTB), to guide the inductive bias of image degradation processes under nonhomogeneous media distributions. SAAB focuses on different spatial locations, capturing more spatial-aware cues to correct color deviations and recover details. MSTB utilizes the difference and complementarity between features at different scales to effectively complement the network’s structural and global perceptual capabilities, enhancing image sharpness and contrast further. Experimental results reveal that the proposed DIMN exceeds most state-of-the-art UIR methods. Our code and results are available at: https://github.com/wwaannggllii/DIMN.

Underwater image restoration method combining color compensation and dual background light fusion

Underwater image restoration method combining color compensation and dual background light fusion

Underwater image recovery based on water type estimation and adaptive color correction

In underwater environments, light propagation is affected by scattering and absorption, leading to color distortion and quality degradation of underwater images. In addition, the presence of a color cast in the image and variations in the attenuation coefficients across various water bodies bring great challenges for underwater image restoration. In this paper, an underwater image restoration method based on water body type estimation and adaptive color correction is proposed. Initially, the underwater images are categorized into color casts and non-color casts according to their hue, and a water body type estimation method based on image color and blurriness is introduced for improving the accuracy of transmission map estimation. Following this, we performed adaptive color correction on the image using a nonlinear transformation, which effectively eliminates color cast. Then the background light position is corrected using the degree of color cast of the image to restore the hue and brightness of the image more naturally. Ultimately, the acquired background light and transmission map are utilized to generate clear underwater images using the image formation model (IFM). Experiments on the widely used UIEB benchmark and SUID datasets show that our method effectively solves the problems of image color distortion and quality degradation, generating satisfactory visual effects.

Underwater image restoration with multi-scale shallow feature extraction and detail enhancement network

Underwater imaging and image processing techniques have recently attracted increasing attention in underwater target detection, archaeology, resource exploration, etc. However, the captured underwater images usually suffer from colour deviation (blue, green, yellow, etc.) and detail blurring problems due to the light absorption, light scattering effects, noises and target occlusions in the water. To solve the above two degradation problems, we propose an underwater image restoration method with multi-scale shallow feature extraction and detail enhancement network. Specifically, we develop a multi-scale shallow feature extraction module to address local detail blurring and alleviate colour deviation issues. Additionally, we design a local detail enhancement module based on an encoder-decoder structure. Quantitative and qualitative experiment results indicate that the proposed method outperforms recently reported state-of-the-art deep-learning-based underwater image restoration methods on four public underwater image datasets. Besides, we implement many ablation experiments to validate the effectiveness of each module in the proposed network.

Underwater Image Restoration Method Based on Multi-Frame Image under Artificial Light Source

This paper studies the underwater image restoration problem in autonomous operation of AUV guided by underwater visual. An improved underwater image restoration method is developed based on multi-frame neighboring images under artificial light source. At first, multi-frame neighboring images are collected during AUV approaching the targets, and a transmittance estimation method is developed based on the multi-frame images to avoid the assumption of the known normalized residual energy ratio in the traditional methods. Then, the foreground and background regions of the images are segmented by locking the small area where the background light is located. Hence, the accuracy of background light estimation is improved for the underwater mages in turbid water to improve the accuracy of image restoration. Finally, the performance of the developed underwater image restoration method is verified by the comparative results in the pool environment.

Underwater image restoration based on adaptive parameter optimization of the physical model.

Underwater images have the advantage of carrying high information density and are widely used for marine information acquisition. Due to the complex underwater environment, the captured images are often unsatisfactory and often suffer from color distortion, low contrast, and blurred details. Physical model-based methods are often used in relevant studies to obtain clear underwater images; however, water selectively absorbs light, making the use of a priori knowledge-based methods no longer applicable and thus rendering the restoration of underwater images ineffective. Therefore, this paper proposes an underwater image restoration method based on adaptive parameter optimization of the physical model. Firstly, an adaptive color constancy algorithm is designed to estimate the background light value of underwater image, which effectively guarantees the color and brightness of underwater image. Secondly, aiming at the problem of halo and edge blur in underwater images, a smoothness and uniformity transmittance estimation algorithm is proposed to make the estimated transmittance smooth and uniform, and eliminate the halo and blur of the image. Then, in order to further smooth the edge and texture details of the underwater image, a transmittance optimization algorithm for smoothing edge and texture details is proposed to make the obtained scene transmittance more natural. Finally, combined with the underwater image imaging model and histogram equalization algorithm, the image blurring is eliminated and more image details are retained. The qualitative and quantitative evaluation on the underwater image dataset (UIEBD) shows that the proposed method has obvious advantages in color restoration, contrast and comprehensive effect, and has achieved remarkable results in application testing. It shows that the proposed method can effectively restore underwater degraded images and provide a theoretical basis for the construction of underwater imaging models.

An underwater image restoration based on global polarization effects of underwater scene

Currently, most underwater polarization imaging methods assume that the backscattering is polarized and the reflected light of the target is unpolarized. In reality, this assumption is unreliable since both backscattering and reflected light contribute to polarization. Therefore, we propose an underwater image restoration method that combines the polarization effects of reflected light and backscattering. Firstly, Stokes vectors are used to automatically obtain polarization difference images. Based on the enhancement measure evaluation (EME) constraint, the initial estimate of backscattering is obtained. Then, an adaptive local optimization (ALO) method is designed to estimate global backscattering light. A variable factor is introduced for local optimization to obtain global backscattering. Finally, in order to optimize the system, particle swarm optimization (PSO) is introduced, which improves the processing speed of the system. The experimental results show that it is necessary to consider the polarization of the target during underwater polarization descattering, which not only has a good ability to eliminate scattering but also has an excellent effect on restoring target details.

Underwater Optical Image Restoration Method for Natural/Artificial Light

This paper investigates the underwater optical image restoration method under the background of underwater target detection based on optical vision in AUVs. The light source used for AUV detection is different when the AUV operates in different depths. The natural light source is used in shallow water and the artificial light source is used in deep water. This paper investigates underwater optical image restoration in these two light conditions. Aiming at the problem of image blurring in underwater optical images, the traditional underwater image restoration method based on scattering model can obtain satisfactory image restoration performance in natural light conditions. However, it cannot obtain the same image restoration result in artificial light conditions. To solve this problem, this paper presents an improved underwater optical image restoration method based on the scattering model. The scattering model and power spectrum are used to solve the initial parameters of the filter, and the parameters are optimized based on an evaluation index. The index of image definition is introduced to evaluate the restoration performance and to achieve the satisfactory image restoration result in both natural light and artificial light conditions. The effectiveness of the presented method is verified by experiments.

An Underwater Image Restoration Method Based on Adaptive Brightness Improvement and Local Image Descattering

An Underwater Image Restoration Method Based on Adaptive Brightness Improvement and Local Image Descattering

Turbid Underwater Image Enhancement Based on Parameter-Tuned Stochastic Resonance

In turbid water, the attenuation and scattering of light caused by scatterers make underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images. To address such problems, a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed in this article. First, an SR algorithm framework for underwater image enhancement is constructed, including the dimensionality reduction and normalization of input images, the solution and parameter optimization of the SR system, the dimensionality upgrading of output images, etc. This framework can apply the SR's ability to enhance weak signals to the enhancement of turbid underwater images. Second, to measure the performance of the system, a synthetic turbid underwater image data set (UWCHIC) is constructed using the underwater imaging model and an image set with simulated scatterers. Based on this data set, the relationship between various image quality evaluation metrics and system parameters is analyzed, and then the suitable no-reference (NR) metrics for system performance evaluation are selected and an adaptive parameter tuning strategy of the SR system is proposed to guide the image enhancement. Lastly, the proposed method is evaluated on the UWCHIC, a dataset to evaluate underwater image restoration methods (TURBID), marine underwater environment database (MUED), and underwater image enhancement benchmark (UIEB) data sets and the turbid underwater images captured from natural waters. Different experimental evaluations demonstrated that the proposed method not only effectively enhances the visual quality of turbid underwater images but also improves the performance of downstream vision tasks.

Underwater image restoration for seafloor targets with hybrid attention mechanisms and conditional generative adversarial network

Underwater image restoration is of great significance in underwater vision research. Affected by the underwater scene, the original underwater image usually has problems such as the color deviation, underexposure, noise, blur and less effective information. To address the above problems, we present an underwater image restoration method for seafloor targets with a hybrid attention mechanism-based conditional generative adversarial network. Firstly, we design a U-net generator network for encoding and decoding the input image. In the skip connections of U-net, we develop a hybrid attention mechanism module that includes spatial and channel attentions to enhance the depth of the network. Then, a multi-modal loss function is designed in the generator network, which takes into account the global content, color, local texture, image gradient, and style information. Qualitative and quantitative evaluations on the reconstructed images are conducted by comparing the proposed method with many previously published works. Experimental results demonstrate that the proposed method generates more visually appealing images and provides higher objective evaluation index scores. Furthermore, ablation experiments are implemented to verify the effectiveness of the proposed hybrid attention module. Noise robustness experiments show that the proposed method also has good denoising ability. Application testing experiments prove that the proposed method has a good application prospect in the seafloor target detection area.

Unsupervised Underwater Image Restoration: From a Homology Perspective

Underwater images suffer from degradation due to light scattering and absorption. It remains challenging to restore such degraded images using deep neural networks since real-world paired data is scarcely available while synthetic paired data cannot approximate real-world data perfectly. In this paper, we propose an UnSupervised Underwater Image Restoration method (USUIR) by leveraging the homology property between a raw underwater image and a re-degraded image. Specifically, USUIR first estimates three latent components of the raw underwater image, i.e., the global background light, the transmission map, and the scene radiance (the clean image). Then, a re-degraded image is generated by randomly mixing up the estimated scene radiance and the raw underwater image. We demonstrate that imposing a homology constraint between the raw underwater image and the re-degraded image is equivalent to minimizing the restoration error and hence can be used for the unsupervised restoration. Extensive experiments show that USUIR achieves promising performance in both inference time and restoration quality.

Underwater image restoration using level adjustment and colour equalization

Due to the light absorption and scattering by water, the underwater image quality is often severely degraded. To solve this problem, we propose an underwater image restoration method that uses level adjustment and colour equalization algorithms to obtain high-quality underwater images. A human visual perception-based underwater imaging model is first built to generate images with high contrast and rich colours. Then, a level adjustment algorithm is developed to improve the brightness of the image. Finally, a colour equalization algorithm is designed to filter the local characteristics and achieve image brightness, colour, and contrast adjustment with nonlinear and filtered local characteristics. Extensive numerical and experimental results show that the proposed method can improve the underwater images and is superior to the recently reported methods.

Multi-prior underwater image restoration method via adaptive transmission.

Captured underwater images usually suffer from severe color cast and low contrast due to wavelength-dependent light absorption and scattering. These degradation issues affect the accuracy of target detection and visual understanding. The underwater image formation model is widely used to improve the visual quality of underwater images. Accurate transmission map and background light estimation are the keys to obtaining clear images. We develop a multi-priors underwater image restoration method with adaptive transmission (MUAT). Concretely, we first propose a calculation method of the dominant channel transmission to cope with pixel interference, which combines two priors of the difference between atmospheric light and pixel values and the difference between the red channel and the blue-green channel. Besides, the attenuation ratio between the superior and inferior channels is adaptively calculated with the background light to solve the color distortion and detail blur caused by the imaging distance. Ultimately, the global white balance method is introduced to solve the color distortion. Experiments on several underwater scene images show that our method obtains accurate transmission and yields better visual results than state-of-the-art methods.

Single underwater image restoration based on descattering and color correction

Underwater images typically exhibit color distortion and low contrast due to the absorption and scattering of the non-uniform medium during the propagation of light underwater. To address these problems, an effective underwater image restoration method is presented in this paper. Firstly, we adopted an adaptive dark channel prior that can be applied to underwater images with different color tones. Then, the local water light with strong robustness is obtained by utilizing maximum filtering and Gaussian low-pass filtering. Additionally, a linear relationship between blurred and clear images in the dark channel was introduced combining with the attenuation coefficient compensation to estimate the transmission of r, g and b channels. Ultimately, we designed a new adaptive color compensation method by taking into account the attenuation difference of the three color channels. Simulation and experimental results show that the proposed method can effectively improve the clarity and contrast of the image and eliminate color distortion.

Underwater image restoration by red channel compensation and underwater median dark channel prior.

Underwater images often show low contrast, blurring, and color distortion due to the absorption and scattering of light. In contrast to existing underwater image restoration methods, we propose an underwater image restoration method with red channel compensation and blue-green channel restoration. First, a proposed approach relies on the hue and attenuation differences between different color channels of the underwater image to estimate the background light. Then, the red channel is enhanced according to a perfect reflection assumption algorithm. Finally, a new median underwater dark channel prior (MUDCP) is proposed to precisely estimate the blue-green channel transmission map. Experimental results show that our method significantly improves contrast, removes color bias, and preserves more detail than other underwater restoration techniques.

Underwater Image Restoration via Information Distribution and Light Scattering Prior

Underwater images suffer from blurred details, color cast, and contrast reduction, caused by absorption and scattering of light. To address these degradation issues, we propose a novel underwater image restoration method based on information distribution and light scattering prior. Concretely, we first estimate the background light according to the information distribution and light scattering characteristics of the background light area. Then, the scene depth map and transmission map are obtained by the relationship between the brightness information and color attenuation. Finally, the underwater image formation model is inversed to obtain the restored image. Extensive experiments indicate that our method can effectively remove the color cast and increase contrast. Furthermore, compared with several state-of-the-art methods, the proposed method yields better results. The qualitative and quantitative results demonstrate that our approach is superior to other comparison methods.

Underwater Single-Image Restoration with Transmission Estimation Using Color Constancy

The issue of underwater image restoration was investigated in this paper. Specifically, the color constancy of a single image was used to estimate the transmission map (TM), which can be used in the image formation model to restore the underwater image. First, the illumination component based on color constancy was used to estimate the refined TM without performing the guided filter or soft matting operation. Second, the statistical property of the pixel was used to fine-tune the color unbalance of underwater images. Finally, both qualitative and quantitative experimental results showed that the proposed method can not only obtain better restoration results, but also improve the real-time performance in different underwater scenes compared with other underwater image restoration methods.