Enhancement of Underwater Images using Texture Distribution Mapping Method

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 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.

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.

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.

In ocean engineering, an underwater vehicle is widely used as an important equipment to explore the ocean. However, due to the reflection and attenuation of light when propagating in water, the images captured by the visual system of an underwater vehicle in the complex underwater environment usually suffer from low visibility, blurred details, and color distortion. To solve this problem, in this article, we present an underwater image enhancement framework based on transfer learning, which consists of a domain transformation module and an image enhancement module. The two modules, respectively, perform color correction and image enhancement, effectively transferring in-air image dehazing to underwater image enhancement. To maintain the physical properties of an underwater image, we embed the physical model into the domain transformation module which ensures that the transformed image complies with the physical model. To effectively remove the color deviation, a coarse-grained similarity calculation is added to the domain transformation module to improve the model performance. The experimental results on real-world underwater images of different scenes show that the presented method is superior to some advanced underwater image enhancement algorithms both qualitatively and quantitatively. Furthermore, we conduct ablation experiments to indicate the contribution of each component and further validate the effectiveness of the presented method through application tests.

Underwater image enhancement and object recognition are crucial in multiple fields, like marine biology, archeology, and environmental monitoring, but face severe challenges due to low light, color distortion, and reduced contrast in underwater environments. DeepSeaNet re-evaluates the model guidance strategy from multiple dimensions, enhances color recovery using the MCOLE score, and addresses the problem of inconsistent attenuation across different regions of underwater images by integrating a feature extraction method guided by a global attention mechanism by ViT. Comprehensive tests on diverse underwater datasets show that DeepSeaNet achieves a maximum PSNR of 28.96 dB and an average SSIM of 0.901, representing a 20–40% improvement over baseline methods. These results highlight DeepSeaNet’s superior performance in enhancing image clarity, color richness, and contrast, making it a remarkably effective instrument for underwater image processing and analysis.

Underwater image enhancement has drawn much attention due to the significance of underwater vision. Although considerable progress has been made in this field, a key problem remains unsolved: how can we extract and enhance minutiae while trying to remove the noise caused by scattering and attenuation? To address this limitation, we propose a new underwater image enhancement technique with a novel spiral generative adversarial framework, named Spiral-GAN, which can effectivelly recover real-world underwater images with more details, vivid colors and better contrast. For steady training and color correction, we include the pixelwise losses that consist of a mean squared error and an angle error in our objective function. In addition, we design our generator with several deconv-conv blocks to preserve the details from the original distorted images. Furthermore, we present a spiral learning strategy for generalizing the enhancing model to effectively recover the real-world underwater images. Finally, we perform a number of qualitative and quantitative evaluations that suggest that our proposed approach can efficiently enhance the quality of underwater images, which can be further used for underwater object detection.

Region-specialized underwater image restoration in inhomogeneous optical environments

Underwater polarimetric imaging for visibility enhancement utilizing active unpolarized illumination

Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method.

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 goal of underwater image enhancement (UIE) is to boost the acquired underwater image quality, which increases the value of the underwater image significantly. However, without effective underwater enhanced image quality assessment (UEIQA) measures that benchmark the UIE, the process of UIE becomes driftless and the enhanced results of different UIE algorithms cannot be fairly compared. Toward this end, we in this work construct a dedicated UEIQA scheme on the basis of deep investigation of the underwater enhanced image characteristics. Specifically, in our proposed method, we respectively design deep neural networks to represent the unique attributes of the underwater enhanced image, such as color cast, local distortions, naturalness degree, sharpness, contrast, fog density, etc., that are highly correlated with the image quality. Then we introduce the Vision Transformer (ViT) to capture the dependencies among different image attributes and infer the image quality level. Extensive experiments conducted on three typical UEIQA databases, i.e., SOTA, UID2021 and SAUD, show that the proposed UEIQA model yields noteworthy higher prediction accuracy than the representative IQA and UEIQA metrics, e.g., achieving SRCC values of 0.891 ( vs. 0.749 in SAUD) and 0.933 ( vs. 0.798 in UID2021). The proposed UEIQA model will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YT2015?tab=repositories</uri>.

Underwater images generally suffer from low contrast, serious noise and color distortion. The main challenges of underwater image enhancement are to preserve details in dark regions while avoiding oversaturetion in bright regions. This paper proposes a novel underwater image enhancement method based on image decomposition. By decomposing the high-frequency texture and noise into the texture layer, the transmission map is estimated from the noise-free structure layer to avoid the noise amplification problem in underwater image enhancement. Both the structure layer and texture layer are descattered with the estimated transmission map. After denoising by gradient residual minimizition, the texture layer is enhanced and added back into the structure layer to recover the final enhanced image. Experimental results verify that the proposed approach can recover the high-quality images with fine details and edges while improving contrast and color naturalness, especially for images taken in the high turbidity environment.

Underwater image enhancement based on conditional generative adversarial network

The underwater color disparity is an important cue for enhancing an underwater image. Applying the underwater color disparity indiscriminately to the entire underwater image tends to give rise to foreground-background crosstalk with either excessive foreground or insufficient background enhancement. To address the discriminativeness between underwater color disparities in foreground and background regions, we develop a discriminative underwater image enhancement method empowered by large foundation model technology. We first utilize the Segment Anything Model to generate segmentation masks, dividing the underwater image into foreground and background regions. This enables accurate foreground-background separation. Then, we conduct adaptive color compensation and fusion to improve the color histogram similarity for foreground and background regions separately. This corrects color deviations and improves contrasts in a discriminative manner that avoids the foreground-background crosstalk. Finally, we propose high-frequency edge fusion to extract high-frequency components from both the original underwater image and the fused image, and then fuse these components to obtain the final enhanced image. This eliminates blurred details arising from the discriminative processing of foreground and background regions. Our method represents the pioneering application of large foundation model technology to empower underwater image enhancement. Experimental results indicate that our method outperforms nine state-of-the-art underwater image enhancement methods in visual quality, achieves superior results across five underwater image quality evaluation metrics on three underwater image datasets, and is beneficial for practical applications such as underwater feature matching. We release our code at https://gitee.com/wanghaoupc/UIE SAM.