Quantum Fourier Transform-Based Algorithms for Underwater Image Contrast and Color Correction

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 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.

Recently, the research and exploration of the ocean has attracted the attention of a large number of scholars. Due to the light scattering and absorption in underwater imaging, the fish observation images cause imaging blurred and details lost. To handle the above limitation, an underwater polarization imaging recovery technique is proposed to recover visibility restoration and color correction of images. First, in the CIE-Lab model, morphological filtering is used to repair the missing details of the L channel, and color correction is performed on A channel. Secondly, in the RGB model, adaptive contrast stretching is performed to enhance the contrast of the image. To further improve the image clarity, dark channel dehazing is implemented to reduce the atomization caused by scattering. Extensive experiments demonstrate that the method can significantly enhance the visual effect, clarity, and detail information of the fish image. The restoration and division of the contour information of fish in the image are improved.

Underwater image sharpening and color correction via dataset based on revised underwater image formation model

All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.

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.

In recent days, a lot of study has been done on improving the visual quality of underwater and undersea imaging in submarine and military operations to find the hidden structure designs and sea excursions. This paper proposed an underwater image enhancement method based on colour constancy theory. When we compare with much of the existing research, the time and complexity of the proposed method are low, and it achieves excellent performance. Firstly, we analyse the underwater imaging model and distortion. Then, by compensating the red channel and applying local white balance, a linear transformation is performed. Finally, results are obtained by applying the histogram equalization to the RGB images. We also measure the image quality by taking the parameters PSNR, UIQM, UCIQE, and Entropy. These parameters are compared to proposed and existing approaches; however, our method produces higher image quality.

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.

This paper reports on WaterGAN, a generative adversarial network (GAN) for\ngenerating realistic underwater images from in-air image and depth pairings in\nan unsupervised pipeline used for color correction of monocular underwater\nimages. Cameras onboard autonomous and remotely operated vehicles can capture\nhigh resolution images to map the seafloor, however, underwater image formation\nis subject to the complex process of light propagation through the water\ncolumn. The raw images retrieved are characteristically different than images\ntaken in air due to effects such as absorption and scattering, which cause\nattenuation of light at different rates for different wavelengths. While this\nphysical process is well described theoretically, the model depends on many\nparameters intrinsic to the water column as well as the objects in the scene.\nThese factors make recovery of these parameters difficult without simplifying\nassumptions or field calibration, hence, restoration of underwater images is a\nnon-trivial problem. Deep learning has demonstrated great success in modeling\ncomplex nonlinear systems but requires a large amount of training data, which\nis difficult to compile in deep sea environments. Using WaterGAN, we generate a\nlarge training dataset of paired imagery, both raw underwater and true color\nin-air, as well as depth data. This data serves as input to a novel end-to-end\nnetwork for color correction of monocular underwater images. Due to the\ndepth-dependent water column effects inherent to underwater environments, we\nshow that our end-to-end network implicitly learns a coarse depth estimate of\nthe underwater scene from monocular underwater images. Our proposed pipeline is\nvalidated with testing on real data collected from both a pure water tank and\nfrom underwater surveys in field testing. Source code is made publicly\navailable with sample datasets and pretrained models.\n

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.

Physical characteristics of underwater environments, such as absorption, scattering, and progressive color loss, are only a few of the many factors that cause underwater images to degrade. Additionally, the turbid water and marine plankton influence the degradation of these images. All of these play a major role in the difficulty of extracting features from underwater images. This study aims to develop a new system that combines color correction techniques and weight maps to address the challenges caused by underwater environments. First step: the color correction, which consists of both color compensation and white balance, are used to improve the colors of images. In the second step: a comprehensive enhancement solution has been adopted on the two images that resulted from the first step by performing two different ways, the first image is improved by an image sharpening algorithm, and the gamma correction is used to process the second one. Four weights maps are applied for feature extraction and finally multi-scale fusion process is used to find the final enhanced image. Three types of underwater scenes are used (Bluish, Greenish and Foggy) to assess the suggested work. In addition to evaluating the results visually, a number of statistical metrics (IE, PCQI, AG, UIQM and UCIQE) are used to evaluate the results and compare them with previous works. The results indicate a marked improvement in all types of image.

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 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>.

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.

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.