Research on underwater image enhancement algorithm based on improved DCP

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.

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.

Mapping of underwater environments is a critical task for a range of activities from monitoring coral reef habitats to surveying submerged archaeological sites. While recent advances in methods for terrestrial mapping can achieve dense 3D reconstructions of scenes in real-time, there remains the challenge of transferring these methods to the underwater domain due to characteristic effects on propagation of light through the water column that violate the brightness constancy constraint used in terrestrial techniques. Current state-of-the-art methods for underwater 3D reconstruction exploit a physical model of light propagation underwater to account for such range-dependent effects as scattering and attenuation; however, these methods necessitate careful calibration of attenuation coefficients required by the physical model, or rely on rough estimates of these coefficients from prior lab experiments. The main contribution of this paper is to develop a novel method to achieve simultaneous estimation of attenuation coefficients for color correction during structure recovery of an underwater scene by integrating this estimation directly into the bundle adjustment step, which performs non-linear optimization. To validate the proposed method, an artificial scene is submerged in a pure water tank and surveyed with a stereo camera platform to simulate an underwater robotic survey in a controlled environment. The target structure is imaged in air with an RGB-D sensor to provide ground truth structure and color, and a color calibration board is place in the scene for further reference. Results show that the proposed method can automatically estimate a water-column aware model for color correction of underwater images simultaneously to 3D reconstruction of the submerged scene.

In the facial action unit (Facial Action Unit, AU) recognition process, due to the low occurrence probability of some AUs, the sample imbalance is serious, which severely limits the model recognition performance. Generative adversarial network GAN is an unsupervised learning method. Compared with the autoencoder and autoregressive model in the unsupervised learning method, its advantages are sufficient data fitting, higher efficiency and better generated samples. The original GAN model uses the minimum and maximum (minmax) to continuously optimize the training of the model; the conditional generation adversarial network CGAN adds conditional constraints to the model input to make the generated results controllable and prevent collapse in the model training process. GAN has been widely used in research fields such as image processing, natural language processing NLP, and real-time color correction of underwater images. This paper designs a model based on a conditional generation adversarial network to supplement the minority samples of a specific AU and improve the sample distribution space of the action unit.

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

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.

Underwater polarimetric imaging for visibility enhancement utilizing active unpolarized illumination

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.

For the underwater degenerative image caused by obscure and noise, atmospheric turbulence model is utilized to construct the restoration model. By computing the underwater image quality function which is used to estimate the adaptive degradation model parameter, underwater image is restored through Wiener filters. The connection between image contrast and average gradients and model parameter is analyzed as well. The experiments show that the proposed method exerts powerful effort on restoration of the obscure underwater image caused by scattering. The proposed method has a better improvement on contrast and definition of underwater image than other common preprocessing methods and is especially suitable for further degradation model analysis and objective detection.

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.

Light of long wavelengths is absorbed almost immediately with depth, while remaining photons are scattered by suspended particles, producing images that are bright, color‑distorted, and structurally blurred. Underwater imaging is therefore a grand challenge across diverse marine environments. Existing enhancement methods fail to balance image fidelity and speed or depend on large, environment‑specific training sets that lack generalization. We propose a frequency‑based quantum‑classical pipeline that uses the Quantum Fourier Transform (QFT) to optimize underwater images without extensive data. The luminance and chroma channels are encoded into quantum states; QFT reveals the spectral content. In the quantum frequency domain, we suppress low‑frequency illumination artifacts and enhance high‑frequency edges and fine details. The modified spectrum is decoded back into classical space in a manner compatible with Noisy Intermediate‑Scale Quantum (NISQ) devices. Experiments on standard underwater datasets show significant improvements in visibility, color balance, and edge sharpness compared to traditional Fourier methods and deep‑learning models. This work demonstrates that quantum spectral processing is an efficient, multi‑productive tool for underwater image enhancement, providing substantial gains in underwater visual perception notably, and for marine navigation and research, in challenging visibility conditions for autonomous underwater vehicles.

The existing UAV aerial image de-fog methods have low image contrast after de-fog, the difference between light and dark image is not obvious, leading to poor de-fog effect. Therefore, an aerial image de-fog enhancement method based on dark channel a priori is proposed. The image variance and absolute gradient mean are combined to get the weight coefficients, and the edge pixels are smoothed by using the multiple decomposition form. The image intensity is calculated and the noise is reduced. A convolution neural network is introduced to calculate the atmospheric transmittance in haze. Based on this, dark channel prior algorithm is used to enhance the light and shade difference of aerial photography image and realise the de-fog enhancement of aerial photography image. To verify the performance of the proposed method, simulation experiments are designed which were compared with the existing methods results in better fog-removing effect, higher contrast and shorter time.

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.

The inaccurate estimation of underwater background light and distortion of scene color results in the lower quality of recovered underwater images. To address this problem, the paper proposes an enhancement algorithm for underwater images based on dark channel theory, which is expected to improve the clarity, contrast and color fidelity of underwater images. Different from the works of others, the paper uses underwater 3-D transmittance map to achieve the effect of deblurring and contrast enhancement of underwater images. The experimental results show that our algorithm can significantly improve the quality of underwater images.