An approach to underwater image enhancement based on image structural decomposition

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.

Region-specialized underwater image restoration in inhomogeneous optical environments

Degraded underwater image enhancement is a challenging task. Due to the light scattering and absorption of suspended particles in water, the original underwater image has a low definition, blurred details, and color distortion, thus affecting advanced underwater visual tasks such as underwater target detection. However, the existing methods are difficult to enhance the image details effectively. In this paper, we proposed an effective deep convolutional neural network model of multi-scale spatial and channel attention fusion (MS-SCANet) for underwater image enhancement. First, a new training data set (UIRDs) is constructed from the existing data. Then, a multi-loss function is constructed to enhance the detail of the image. Finally, the performance of the model in image visibility and color correction is discussed. Through experiments and comparative analysis on two test sets, our method is superior to the existing traditional methods and deep learning models in terms of image visibility, detail enhancement, and color correction.

Bayesian retinex underwater image enhancement

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 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.

Vision graph convolutional network for underwater image enhancement

Underwater polarimetric imaging for visibility enhancement utilizing active unpolarized illumination

Underwater applications require clear underwater images, however, blurring and contrast degradation in underwater images are caused by absorption, dispersion, and water mass. To address the limitations of existing color correction and deblurring algorithms, this study proposes a fusion-based image enhancement algorithm. Two images obtained by adaptive channel defogging technique and restricted histogram mapping interval correction technique are used as two inputs to the fusion framework to obtain a new underwater image. We tested our technique with a public dataset. The experimental evidence shows that the technique proposed in this paper can improve the quality and detail of blurred underwater images. This paper also demonstrates that the method has superior deblurring and color correction capabilities than other existing algorithms.

Aimed at the two problems of color deviation and poor visibility of the underwater image, this paper proposes an underwater image enhancement method based on the multi-scale fusion and global stretching of dual-model (MFGS), which does not rely on the underwater optical imaging model. The proposed method consists of three stages: Compared with other color correction algorithms, white-balancing can effectively eliminate the undesirable color deviation caused by medium attenuation, so it is selected to correct the color deviation in the first stage. Then, aimed at the problem of the poor performance of the saliency weight map in the traditional fusion processing, this paper proposed an updated strategy of saliency weight coefficient combining contrast and spatial cues to achieve high-quality fusion. Finally, by analyzing the characteristics of the results of the above steps, it is found that the brightness and clarity need to be further improved. The global stretching of the full channel in the red, green, blue (RGB) model is applied to enhance the color contrast, and the selective stretching of the L channel in the Commission International Eclairage-Lab (CIE-Lab) model is implemented to achieve a better de-hazing effect. Quantitative and qualitative assessments on the underwater image enhancement benchmark dataset (UIEBD) show that the enhanced images of the proposed approach achieve significant and sufficient improvements in color and visibility.

Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multi-scale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods.

Diving deeper into underwater image enhancement: A survey

ABSTRACTThe major problem in underwater image processing involves light attenuation. In order to overcome such light incorporation and dispersion issues, this paper proposes a new underwater image enhancement model that includes certain phases like contrast correction and colour correction. Here, two processes are worked out in contrast correction namely (i) global contrast correction and (ii) local contrast correction. Furthermore, as the histogram evaluation is the major part of any image enhancement, it is very important to choose the optimal limits of the histogram. To solve this optimization problem, this paper introduces a new optimization model namely Distance Oriented Cuckoo Search (DOCS) algorithm that also satisfies the objective function defined in this paper. The major process of this model focusses on Integrated Global and Local Contrast Correction (IGLCC). Finally, the proposed model compares its performance over other methods.

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.

Recent advances in underwater image enhancement (UIE) have achieved notable progress using deep learning techniques; however, existing methods often struggle with limited receptive fields, inadequate frequency modeling, and poor structural perception, leading to sub-optimal visual quality and weak generalization in complex underwater environments. To tackle these issues, we propose FACMamba, a Mamba-based framework augmented with frequency-aware mechanisms, enabling efficient modeling of long-range spatial relations for underwater image restoration. Specifically, FACMamba incorporates three key components: a Multi-Directional Vision State-Space Module (MVSM) to model directional spatial context via the proposed 8-direction selective scan block (SS8D), a Frequency-Aware Guidance Module (FAGM) for learning informative frequency representations with low overhead, and a Structure-Aware Fusion Module (SAFM) to preserve fine-grained structural cues through adaptive multi-scale integration. Recognizing the importance of spatial-frequency interaction, our model fuses these representations via lightweight architecture to enhance both texture and color fidelity. Experiments on standard UIE benchmarks demonstrate that FACMamba achieves a favorable balance between enhancement quality and computational efficiency, outperforming many existing UIE methods.