Underwater image color correction based on surface reflectance statistics

In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazy image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light (BL) and the transmission map (TM). Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior (NUDCP) via the statistic of clear and high resolution (HD) underwater images, then a scene depth map based on the underwater light attenuation prior (ULAP) and an adjusted reversed saturation map (ARSM) are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R and G-B channels. Finally, to improve the color and contrast of the restored image with a dehazed and natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, lower computation time, overall superior performance, and better information retention.

Due to complex underwater environments characterized by severe scattering, absorption, and color distortion, accurate restoration remains challenging. This paper proposes a hybrid approach combining dual transmission estimation, adaptive ambient light estimation with color correction, and a U-Net Transformer (Uformer) for underwater image enhancement. Our method estimates transmission maps by integrating boundary constraints and local contrast, which effectively address visibility degradation. An adaptive ambient light estimation and color correction strategy are further developed to correct color distortion robustly. Subsequently, a Uformer network enhances the restored image by capturing global and local contextual features effectively. Experiments conducted on publicly available underwater image datasets validate our approach. Performance is quantitatively evaluated using widely adopted non-reference image quality metrics, especially Underwater Image Quality Measure (UIQM) and Underwater Color Image Quality Evaluation (UCIQE). The results demonstrate that our proposed method achieves superior enhancement performance over several state-of-the-art methods.

Underwater images often suffer from color distortion and detail loss due to light absorption and scattering, which degrades visual quality and limits practical applications. To address these issues, a novel adaptive algorithm for underwater image color correction and detail enhancement is proposed. The algorithm first applies threshold stretching to adjust the grayscale range, enhancing contrast while mitigating the risk of localized overcompensation. Based on the color distribution, images are categorized into bluish and greenish tones, providing the foundation for the adaptive color compensation method (ACCM). The ACCM is designed to separately compensate different color channels, using the green channel as a reference to restore the most degraded channels while maintaining overall color balance. The compensation process is further constrained by the minimum color loss criterion to ensure consistent color fidelity. Furthermore, an edge detail enhancement method is formulated to recover fine details by amplifying intensity differences between the original image and its smoothed version. Extensive experiments on multiple underwater image data sets demonstrate that the proposed algorithm consistently outperforms state-of-the-art methods, achieving average improvements of 0.0021, 0.0646, 0.3677, and 0.0800 in underwater color image quality evaluation, underwater image quality metric, fog aware density evaluator, and colorfulness contrast fog density index metrics, respectively, underscoring its effectiveness and robustness across diverse underwater environments.

The quality of underwater imaging is significantly degraded by light scattering and absorption due to water body and suspended particles. To address the issues of color distortion and contrast degradation, we propose a novel underwater image restoration method based on constrained color compensation and background light color space-based haze-line model. Our method begins by applying the constrained color compensation approach to provide targeted intensity adjustments for attenuated color channels. This process corrects color distortion while simultaneously mitigating the risks of overcompensation and insufficient color saturation. Subsequently, the haze-line model is employed in the transformed background light color space to restore the underwater image. Specifically, the color corrected underwater image is transformed to a novel color space, where the background light intensity serves as the origin. In this color space, all haze lines are identified by grouping pixels with similar color characteristics through the superpixel clustering method. Then, the transmission distribution can be estimated based on the haze-line model. Finally, the scattered light components are removed by applying the underwater descattering model with the estimated transmission distribution to the luminance channel of the color corrected underwater image. Comparative experiments implemented on the UIEB and UCCS underwater image datasets demonstrate the superiority of the proposed method in terms of color correction and contrast enhancement when compared with state-of-the-art underwater image restoration techniques. Our codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MinjieWan/C3HLM</uri>.

Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of computer vision tasks in underwater environments. Different approaches are proposed in the literature to enhance the underwater images, however the dynamically varying environmental conditions remains challenging. It requires adaptive approach to adjust the color dynamically and to estimate the features. In this study, we propose a novel hybrid approach for underwater image enhancement that integrates a customized color correction algorithm with Recurrent Convolutional Neural Networks (R-CNN), offering superior image enhancement capabilities compared to conventional methods. The proposed method comprises three key stages: color correction, recurrent feature extraction, and enhancement. The color correction algorithm goes beyond traditional method by adjusting the color channels based on dynamically estimated attenuation coefficients specific to underwater environments, effectively restoring natural color tones and also enhancing overall visual accuracy. The recurrent design enables the network to enhance feature representations by continuously refining them across iterations, thus preserving intricate scene details better than static CNNs. Finally, the novel enhancement strategy guided by the recurrently extracted features, further improves the clarity, contrast and visibility. Experimental evaluation conducted on diverse underwater image datasets demonstrate the superiority of proposed approach, offering significant improvements in image quality over state-of-the-art techniques. Qualitative and quantitative analyses reveal that the proposed method not only enhances images aesthetics but also facilitates better performance in downstream computer vision tasks such as object detection, recognition and environmental monitoring.

A novel biologically-inspired method for underwater image enhancement

Contour and texture preservation underwater image restoration via low-rank regularizations

The interference of suspended particles causes the problems of color distortion, haze effect and visibility reduction in complex underwater environment. However, existing methods for enhancement often result in overexposure of the low contrast area or distortion of the seriously turbid area. The main factor is the diversity of the underwater images. In this paper, we propose a novel underwater imaging model which can recover different kinds of underwater images, especially the image from the murky water. Specifically, we develop a reverse dark channel prior algorithm which can separate the image with large degradation by setting a maximum filter dark channel threshold. Using the algorithm, we can calculate the adaptive transmissivity and restore the blurred images. The restored image will be sent into an end-to-end generative model for coloring transformation and enhancement. Furthermore, we build an edge deepening module for edge recovery. Comprehensive experimental evaluations show that our method performs promising on image enhancement for different kinds of turbid water. Compared with other previous methods, our method is more universal and more suitable for practical applications.

Underwater images often suffer from color distortion and visibility degradation due to the light absorption and scattering. Existing methods utilize various assumptions/constrains to achieve reasonable solutions for underwater image enhancement. However, these methods share the common limitation that the adopted assumptions may not work for some particular scenes. To address this problem, this paper proposes an end to end framework for underwater image enhancement, where a CNN-based network called UIE-Net is presented. The UIE-net is trained with two tasks, color correction and haze removal. This unified training approach enables learning a strong feature representation for both tasks simultaneously. For better extracting the inherent features in local patches, a pixels disrupting strategy is exploited in the proposed learning framework, which significantly improves the convergent speed and accuracy. To handle the training of UIE-net, we synthesize 200000 training images based on the physical underwater imaging model. Experiments on benchmark underwater images for cross-scenes show that UIE-net achieves superior performance over existing methods.

A multi-angle image enhancement method based on underwater imaging was proposed to solve the problems of color bias, geometric distortion and low contrast of the observed object in the complex and changeable underwater environment. Firstly, the gray world algorithm is used to preprocess the underwater image. Then an image optimization denoising algorithm based on K-SVD dictionary learning is used to keep the structure and texture information of the original image. Finally, combining with the underwater optical imaging model, the blue-green channel enhancement algorithm is used to realize the underwater imaging defog processing, which makes the restored image closer to the reality. The results show that this method can solve the problems of noise interference, fuzzy underwater image and low contrast.

Images captured by underwater robots often suffer from issues such as blurring and colour distortion, which hinder effective feature extraction and target recognition in underwater environments. To address these challenges, this paper proposes a novel underwater image enhancement method based on generative adversarial networks (GANs), termed multiple colour space underwater generative adversarial network (MCS‐UGAN). The proposed method is built upon a GAN framework, consisting of a generator and a discriminator. The generator comprises two main modules: a deblurring module and a colour correction module. The deblurring module innovatively incorporates an efficient multi‐scale feature extraction technique and an attention mechanism, which enhances object contours while preserving fine image details. The colour correction module integrates residual blocks into the U‐Net architecture, effectively mitigating the problems of gradient vanishing and explosion during backpropagation in underwater image enhancement networks, thereby enhancing the network's feature learning capability. This design corrects colour distortions while preserving edge information in the image. The discriminator adopts the PatchGAN structure, which focuses on the local regions of the image, significantly improving the generator's ability to restore high‐frequency details and thus enhancing the quality of the generated images. Experimental results on benchmark datasets demonstrate that, compared to existing methods, MCS‐UGAN achieves superior performance in terms of peak signal‐to‐noise ratio, structural similarity index measure, underwater image quality measure, and underwater colour image quality evaluation, with average values of 26.24, 0.91, 3.13, and 0.64, respectively. Results from real‐world applications further show that MCS‐UGAN effectively increases the number of extracted corner points, validating its practicality and effectiveness. The code is available at https://github.com/invincibility6/MCS‐UGAN.git

Underwater optical imaging is essential for exploring the underwater environment to provide information for planning and regulating underwater activities in various underwater applications, such as aquaculture farm observation, underwater topographical survey, and underwater infrastructure monitoring. Thus, there is a need to investigate the underwater imaging process and propose clear and long-range underwater optical imaging methods to fulfill the demands of academia and industry. In this manuscript, we classify the eighteen most commonly used underwater optical imaging methods into two groups regarding the imaging principle, (1) hardware and (2) software-based methods, each with an explanation of the theory, features, and applications. Furthermore, we also discuss the current challenges and future directions for improving the performance of current methods, such as improving the accuracy of underwater image formation model estimation, enlarging the underwater image dataset, proposing comprehensive underwater imaging evaluation metrics, estimating underwater depth and integrating different methods (e.g., hardware- and software-based methods for computational imaging) to promote the imaging performance not only in the laboratory but also in practical underwater scenarios.

In marine ecosystems, the geological formation, and aquatic life are analyzed by underwater imaging. The visibility of the aquatic environment is enhanced by the underwater image improvement approaches. Because of the dispersal of the particles, attenuation of light, and distortion of color, the capturing of marine images is a challenging task. The enhanced images with the absence of water turbidity and light attenuation provide an excellent view of the underwater sceneries. The dispersion and incorporation of light underwater cause color distortion effects. Without disturbing the accuracy of the underwater image, the process of obtaining an accurate color representation becomes a complicated task. Overall excellence of the enhanced images may affected by the noise or the artifacts by traditional approaches which tends to affect the image quality. Also, the preservation of important details in the image is a rigorous task while removing the distortion. To combat such issues, an adaptive deep learning-based underwater image co-enhancement is suggested. In the first phase, the images are gathered from the standard dataset. Further, the image is fed into the correlation feature matching module. Here, the feature matching is executed via the Adaptive Trans-Unet (ATUNet), and the correlation feature matching performance of the ATUNet is strengthened by tuning the parameter using the Updated Random Parameter-based Red Kite Optimization Algorithm (URP-RKOA). The usefulness of the developed model is defined using the experimental study. Throughout the analysis, the numerical findings of the developed model attain the values of 155.01, 26.2, and 0.78 in terms of MSE, PSNR, and SSIM. The underwater image co-enhancement achieved better performance rather than existing approaches.

The paper explores the problems of restoring underwater images exposed to distortions in the form of color and contrast deformations, the presence of haze, etc., arising from the interaction of optical radiation with the aquatic environment. Restoring underwater images is a non-trivial task due to the large variability of the parameters of the aquatic environment and photography conditions. The proposed method, unlike other underwater image recovery algorithms based on an imaging model, is not based on a simplified exponential Beer-Lambert law for estimating optical radiation attenuation in water, but on a more accurate physical approach that simulates the propagation of optical rays in water using the Monte Carlo method, taking into account the main parameters the water environment and the camera. The results of numerical simulation of optical ray propagation in an aquatic environment are used for image processing in the spatial domain by editing the histograms of each image channel in the RGB color space. To test the developed algorithm, 6 real underwater images were selected obtained under various lighting conditions (natural and artificial) and various parameters of the aquatic environment (clear ocean and turbid coastal water). For the purpose of qualitative and quantitative analysis of the obtained results, the following similar underwater image processing methods were used: Fusion, UDCP IATP, Retinex, HE, and UWB VCSE. The Underwater Colour Image Quality Evaluation Metric (UCIQE) and Underwater Image Quality Measure (UIQM) indicators were used to quantify the results obtained. The results of the qualitative assessment demonstrate the high efficiency of the proposed method: regardless of the conditions of the initial image parameters, the application of the developed method improves visual perception and does not lead to excessive contrast enhancement, color distortion, loss of detail, the appearance of artifacts, etc. Quantification of underwater image processing results demonstrates comparable and superior results when comparing the efficiency of the algorithm with similar methods. For the UCIQE parameter, the developed method provided an improvement from 9 % to 51 % relative to the parameter value for the original image, while similar methods demonstrated results from minus 10 % to 82 %. For the UIQM parameter, the developed method provided an improvement from 24 % to 99 % relative to the parameter value for the original image, while similar methods demonstrated results from minus 10 % to 123 %. Unlike analogues, the developed method did not demonstrate the worst values of the UCIQE and UIQM parameters for any processed image, which indicates the stability of the method regardless of the parameters of the aquatic environment and shooting conditions. By dividing the developed method into preliminary and main stages, high image processing speed is ensured: 0.073 seconds for images with a resolution of 400 × 300 pixels and from 8.02 to 8.23 seconds for images with a resolution of 5184 × 3456 pixels. Similar methods demonstrated values from 0.19 to 10.81 seconds for an image with a resolution of 400 × 300 pixels and from 7.65 to 937.83 seconds for an image with a resolution of 5184 × 3456 pixels. The introduction of the proposed method into the geological exploration will increase their efficiency and reliability, and will provide more accurate data for further exploration of solid mineral deposits. Such technique integrated into the machine vision system of underwater vehicles will significantly expand their functionality by enabling automation of operations and improving the efficiency of recognition systems.