A novel biologically-inspired method for underwater image enhancement

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.

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.

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.

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.

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.

Degraded images with light scattering and absorption such as haze, underwater and sandstorm images always suffer from low contrast, detail loss, and color distortion. To solve these problems, herein, we propose a multi-stage image restoration method which includes three steps: dehazing, detail preserving and color correction. Dehazing includes estimations of ambient light and transmission map. Ambient light is estimated by fusing scene depth map and high illumination area, and transmission map is modified by adaptive linear transformation. Then, an objective function with Relative Total Variation (RTV) and edge preservation component is proposed to preserve details and suppress noise. Finally, an effective color correction method is introduced to correct color distortion and avoid over-correction. Experiments on haze, sandstorm and underwater images demonstrate that our method can obtain high quality results with high contrast, clear visibility, and natural color. Additional experiments suggest that our method can also enhance low-light images.

Underwater images suffer from the quality degradation caused by the light scattering and absorption in the water. Recent deep-learning based underwater image enhancement methods achieve limited performance due to the lack of proper training datasets. In this paper, we provide a realistic underwater image dataset reflecting the diverse characteristics of turbidity and color distortion in underwater environments. We also design an underwater image enhancement network to exploit the local and global features of the turbid underwater images with color distortion. The experimental results show that the proposed dataset is more effective than existing datasets to train deep neural networks, and the proposed underwater image enhancement network successfully removes the haze and color distortion from real underwater images while outperforming the existing methods.

Region-specialized underwater image restoration in inhomogeneous optical environments

Underwater image processing has attracted much interest during the past decades. Most of the underwater images suffer from the problems of backscattering and color distortion. In this paper, we focus on solving the problem of color distortion. Due to the light attenuation, which is caused by absorption and scattering, different colors of light will disappear gradually with the increase of water depth according to their wavelengths. The blue color has the shortest wavelength, so it can reach the largest depth, which results in the bluish tone of the underwater images. Our main contribution is that we proposed a new color correction scheme based on a local surface statistical prior. Our work mainly contains two steps. Firstly, we segment the underwater image into several non-overlapped blocks. Secondly, for each block, we estimate its illuminant based on the image formation model and the local surface statistical prior. By dividing the image block by its illuminant, the true reflectance can be obtained. Our experimental results demonstrate that our proposed method can achieve comparable or even better results than some state of the art approaches.

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

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.

Bayesian retinex underwater image enhancement

Underwater images suffer from severe color distortion, due to the wavelength-dependent light attenuation and scattering. Various underwater image enhancement methods have been developed to improve the quality of degraded underwater images. However, contemporary approaches often overlook the impact of different scene colors on the overall process, potentially leading to undesired outcomes, such as enhanced images exhibiting excessive redness. In this paper, we observe that the color tones of degraded underwater images exhibit variability under the influence of different underwater targets and scenes. Each degraded color channel can be utilized to guide the color correction of other channels. Given this, a light-weight underwater color correction network, dubbed UCCNet, is presented to alleviate the issue of color corruption. In UCCNet, three parallel branches are designed to excavate the residual information within each color channel, subsequently leveraging these features to improve the quality of underwater images. Moreover, facing the challenge of effectively enhancing underwater images in diverse and complex scenes, the model UCCNet-KT is established based on UCCNet. In UCCNet-KT, the technology of knowledge transfer is designed to improve the generalization ability by enriching the dataset and constructing the loss function. Extensive experiments on various underwater datasets indicate the impressive performance of the UCCNet and UCCNet-KT qualitatively and quantitatively.

Underwater images often suffer from color distortion, reduced contrast, and blurriness due to light refraction, absorption, and scattering. In this paper, we propose a coarse-to-fine deep <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Py</b>ramid network for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</b>nderwater <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b>mage <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b>nhancement (PyUIE). Specifically, PyUIE begins by decomposing the input image into high- and low-frequency components using a Laplacian pyramid. The low-frequency residual, which primarily contains lighting and color information, is processed with a lightweight deterministic color mapping network to correct global illumination and color distortions. Concurrently, the high-frequency components containing the fine details are enhanced in a coarse-to-fine manner, such that each higher scale is guided by the reconstruction from the adjacent lower scale. This hierarchical strategy effectively mitigates the risk of over-enhancement by avoiding excessive modifications to the high-frequency components. Additionally, we implement a multi-scale supervised training strategy, enabling the model to learn and reconstruct features across multiple scales, which enhances its ability to capture diverse details and improves its generalization and robustness. Extensive experiments demonstrate that our method successfully restores fine details and small structures in underwater images while producing vivid and visually appealing colors, thereby outperforming existing enhancement methods in both qualitative and quantitative evaluations. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ttttllt/PyUIE.git</uri>.