Underwater Image Enhancement Algorithm Research Articles

Domain Adaptation for Underwater Image Enhancement via Content and Style Separation

Underwater image suffer from color cast, low contrast and hazy effect due to light absorption, refraction and scattering, which degraded the high-level application, e.g, object detection and object tracking. Recent learning-based methods demonstrate astonishing performance on underwater image enhancement, however, most of these works use synthetic pair data for supervised learning and ignore the domain gap to real-world data. To solve this problem, we propose a domain adaptation framework for underwater image enhancement via content and style separation, different from prior works of domain adaptation for underwater image enhancement, which target to minimize the latent discrepancy of synthesis and real-world data, we aim to separate encoded feature into content and style latent and distinguish style latent from different domains, i.e. synthesis, real-world underwater and clean domain, and process domain adaptation and image enhancement in latent space. By latent manipulation, our model provide a user interact interface to adjust different enhanced level for continuous change. Experiment on various public real-world underwater benchmarks demonstrate that the proposed framework is capable to perform domain adaptation for underwater image enhancement and outperform various state-of-the-art underwater image enhancement algorithms in quantity and quality. The model and source code will be available at https://github.com/fordevoted/UIESS

Human Perceptual Quality Driven Underwater Image Enhancement Framework

Underwater images suffer from severe color casts, low contrast, and blurriness, which greatly degrade the visibility and color fidelity of underwater images. Recently, numerous underwater image enhancement (UIE) algorithms have been proposed. Existing synthetic datasets-based deep learning methods employ synthetic datasets to train UIE models. However, there is a gap between synthetic datasets and real underwater images, leading to poor generalization of synthetic datasets-based UIE methods. Besides, existing real datasets-based deep learning methods largely focus on minimizing the mean squared reconstruction error between UIE results and corresponding ground-truth on the real datasets, but do not take human visual perception into account. Thus, although they achieve high PSNR between UIE results and corresponding ground-truth obtained by user study on the real datasets, they often achieve unsatisfactory perceptual quality. To address these problems, we propose a Human Perceptual Quality Driven Underwater Image Enhancement Framework (HPQ-UIEF) to achieve better results in human perceptual quality and maintain satisfactory PSNR, which is trained on a real underwater enhancement quality assessment database (UEQAB). Specifically, an Underwater Image Quality Assessment Network (UIQAN) for UIE images is first proposed to assist UIE task, in which a novel depth map prior spatial attention block (DPPAB) is embedded into UIQAN. The DPPAB can adaptively recalibrate the quality-aware feature maps and model human visual attention in a data-driven manner. Then, the UIE model is proposed, in which the UIQAN is introduced as the loss function to optimize our UIE model in the direction of perceptual metrics. Moreover, since the confidence map acquired by UIQAN can effectively reflect the sensitivity of human perceptual of local area in an UIE image, the confidence map is introduced to our UIEF to help our UIEF to perceive the perceptually important regions. Thus, the confidence map is down-sampled and then concatenated into the decoder module of the UIE model, which can further improve the perceptual quality of the UIE results. Extensive experimental results show that the proposed HPQ-UIEF outperforms state-of-the-art UIE methods qualitatively and quantitatively.

Twin Adversarial Contrastive Learning for Underwater Image Enhancement and Beyond.

Underwater images suffer from severe distortion, which degrades the accuracy of object detection performed in an underwater environment. Existing underwater image enhancement algorithms focus on the restoration of contrast and scene reflection. In practice, the enhanced images may not benefit the effectiveness of detection and even lead to a severe performance drop. In this paper, we propose an object-guided twin adversarial contrastive learning based underwater enhancement method to achieve both visual-friendly and task-orientated enhancement. Concretely, we first develop a bilateral constrained closed-loop adversarial enhancement module, which eases the requirement of paired data with the unsupervised manner and preserves more informative features by coupling with the twin inverse mapping. In addition, to confer the restored images with a more realistic appearance, we also adopt the contrastive cues in the training phase. To narrow the gap between visually-oriented and detection-favorable target images, a task-aware feedback module is embedded in the enhancement process, where the coherent gradient information of the detector is incorporated to guide the enhancement towards the detection-pleasing direction. To validate the performance, we allocate a series of prolific detectors into our framework. Extensive experiments demonstrate that the enhanced results of our method show remarkable amelioration in visual quality, the accuracy of different detectors conducted on our enhanced images has been promoted notably. Moreover, we also conduct a study on semantic segmentation to illustrate how object guidance improves high-level tasks. Code and models are available at https://github.com/Jzy2017/TACL.

Underwater Image Enhancement Method Based on Feature Fusion Neural Network

Aiming at the problems of uneven illumination of underwater image caused by supplementary illumination in deep-sea and night waters, image noise, low contrast and color deviation caused by suspended particles in water, a new underwater image enhancement method under non-uniform illumination is proposed. The heterogeneous feature fusion module is designed to fuse different levels and different levels of features, so as to improve the overall perception ability of the network to detail information and semantic information. Secondly, a new feature attention mechanism is designed to improve the traditional channel attention mechanism, and the improved channel attention and pixel attention mechanism are added to the heterogeneous feature fusion process to strengthen the ability of the network to extract pixel features with different turbidity. Then, the dynamic feature enhancement module is designed to adaptively expand the receptive field to improve the adaptability of the network to the image distortion scene and the ability of model conversion, and strengthen the network’s learning of the region of interest. Finally, the color loss function is designed, and the absolute error loss and structural similarity loss are jointly minimized to correct the color deviation on the basis of maintaining the image texture. A multi-scale feature extraction module is designed to extract different levels of features at the beginning of the network, and the output results are obtained through the convolution layer with jump connection and the attention module. The experimental results on several data sets show that this method can have good results in processing synthetic underwater images and real underwater images, and can better restore the image color and texture details compared with the existing methods. It conforms to the characteristics of human vision, and the visual effect is better than the existing underwater image enhancement algorithms.

Twice Mixing: A rank learning based quality assessment approach for underwater image enhancement

Objectively and accurately evaluating underwater images generated by different enhancement algorithms is an essential issue, which however is still largely under-explored. In this paper, we present a novel rank learning guided no-reference quality assessment method to evaluate different underwater image enhancement (UIE) algorithms. It is also the first work that utilizes deep learning approaches to address this problem. Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image and its low-quality version. Twice Mixing is trained based on an elaborately formulated self-supervision mechanism. Specifically, before each iteration, we randomly generate two mixing ratios which will be utilized for both generating virtual images and guiding the network training. In the test phase, a single branch of the network is extracted to predict the quality rankings of different UIE outputs. Additionally, to train our network, we construct a new dataset that contains over 2200 raw underwater images and their high/low-quality versions. Twice Mixing is evaluated on both synthetic and real-world datasets. Experimental results show that the proposed approach outperforms the previous methods significantly.

Research on underwater image enhancement algorithm based on improved DCP

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.

Underwater image enhancement method with non-uniform illumination based on Retinex and ADMM

In order to solve the image blurring and distortion problem caused by underwater non-uniform and low illumination, this paper proposes an underwater image enhancement algorithm based on the Retinex theory and the Alternating Direction Method of Multipliers (ADMM). Firstly, the L component of the original image in the Lab space is extracted as the initial illumination map, and an Augmented Lagrange Multiplier (ALM) framework is constructed based on the ADMM to optimize the initial illumination map in order to obtain an accurate illumination image. In addition, the illumination map is further corrected in the luminance region with the Gamma Correction. Secondly, combined with the color constancy characteristics in the Retinex theory, the reflected image of the object is obtained. Finally, the bilateral filter is picked to suppress the underwater noise and obtain a more detailed enhanced image. The experimental results show that the underwater image enhancement algorithm can effectively solve the non-uniform illumination problem caused by natural light or artificial light source and improve the underwater image quality, thus having a better performance than other algorithms.

An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

A new underwater image enhancement algorithm based on adaptive feedback and Retinex algorithm

A new underwater image enhancement algorithm based on adaptive feedback and Retinex algorithm

A Comparative Study of Underwater Marine Products Detection based on YOLOv5 and Underwater Image Enhancement

The application of target detection algorithms in underwater images has not been very effective due to the quality problems such as image blurring and color incongruity that often exist in underwater images. In this paper, the YOLOv5 algorithm is used as a target detection network model, which is trained by using underwater marine products dataset and combined with six underwater image enhancement recovery algorithms to enhance and recover the images before they are detected, an attempt is made to improve the deep learning based target detection method at the input side. Finally, the impact of different underwater image enhancement algorithms on the YOLOv5-based target detection algorithm is compared and summarized through experiments, and the specific image enhancement methods can effectively improve the detection performance of the algorithm under different image background environments.

Weakly supervised underwater image enhancement algorithm incorporating attention mechanism

weakly supervised underwater image enhancement algorithm incorporating attention mechanism

Underwater Image Co-Enhancement With Correlation Feature Matching and Joint Learning

In underwater scenes, degraded underwater images caused by wavelength-dependent light absorption and scattering present huge challenges to vision tasks. Underwater image enhancement has attracted much attention due to the significance of vision-based applications in marine engineering and underwater robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, almost all existing approaches focus only on the enhancement of independent images. Considering that images photographed in the same underwater scene usually share similar degradation, related images can provide rich complementary information for each other’s enhancement. In this paper, we propose an Underwater Image Co-enhancement Network (UICoE-Net) based on an encoder-decoder Siamese architecture. For joint learning, we introduced correlation feature matching units into the multiple layers of our Siamese encoder-decoder structure in order to communicate the mutual correlation of the two branches. Extensive experiments using the Underwater Image Enhancement Benchmark (UIEB), Underwater Image Co-enhancement Dataset (UICoD) collected from an underwater video dataset with ground-truth reference and Stereo Quantitative Underwater Image Dataset (SQUID) dataset demonstrate the effectiveness of our method.

DewaterNet: A fusion adversarial real underwater image enhancement network

Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different datasets and metrics. In this paper, we utilize an effective and public underwater benchmark dataset including diverse underwater degradation scenes to enlarge the test scale and propose a fusion adversarial network for enhancing real underwater images. Meanwhile, the multiple inputs and well-designed multi-term adversarial loss can not only introduce multiple input image features, but also balance the impact of multi-term loss functions. The proposed network tested on the benchmark dataset achieves better or comparable performance than the other state-of-the-art methods in terms of qualitative and quantitative evaluations. Moreover, the ablation study experimentally validates the contributions of each component and hyper-parameter setting of loss functions.

UIEC^2-Net: CNN-based underwater image enhancement using two color space

Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the past few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN. To our best knowledge, this method is the first one to use HSV color space for underwater image enhancement based on deep learning. UIEC^2-Net is an end-to-end trainable network, consisting of three blocks as follow: a RGB pixel-level block implements fundamental operations such as denoising and removing color cast, a HSV global-adjust block for globally adjusting underwater image luminance, color and saturation by adopting a novel neural curve layer, and an attention map block for combining the advantages of RGB and HSV block output images by distributing weight to each pixel. Experimental results on synthetic and real-world underwater images show that the proposed method has good performance in both subjective comparisons and objective metrics. The code is available at https://github.com/BIGWangYuDong/UWEnhancement.

Underwater images contrast enhancement and its challenges: a survey

Exploration of the deep sea and ocean in the marine industry has continued to gain interest in recent years. To get the detailed imaging of deep sea layers, marine vessels and robots are fitted with advanced imaging technologies. There are certain factors like water properties and impurities that affect the quality of the photographs captured by the underwater imaging devices. As sea water absorbs colors, so processing of sea imaging data becomes more challenging. Water light attenuation is a phenomenon that is caused by the absorbance and scattering factors. Certain studies showed that the existence of certain intrinsic shortcomings are attributed to the appearance of objects and ambient noise in underwater images. As a result, it is difficult in a real-time system to distinguish objects from their surroundings in these images. We measures the algorithms performance with respect to various aspects, effect of the hardware and software parts for underwater images and critical review of different underwater image enhancement algorithms. First, we describe some well-known techniques of spatial and frequency domains. Then, we list the existing quantitative measurements which are required to measure the quality of the enhanced image. Finally, the performance of various up-to-date existing methods is compared based on the outcomes of standard quantitative measurements, and factors such as requirements/suitability, and technical aspects, are included. Furthermore, a variety of image databases used for image contrast enhancement is discussed in detail. This study expands the scope for other researchers to understand the important characteristics of different underwater image contrast enhancement methods, and also provides future research directions.

A novel underwater sonar image enhancement algorithm based on approximation spaces of random sets

Underwater environment is complex and random. The images obtained from underwater by sonar always have uneven background gray distribution and fuzzy details of boundary. Hence the low-quality sonar images need to be enhanced before analysis. This paper presents a sonar image enhancement algorithm based on the approximation spaces of random sets. First of all, the knowledge representation of the underwater image is constructed by the approximation spaces of random sets. According to the background knowledge, the image is divided by the upper and lower approximation space of the random set. Then the optimal partition is obtained according to the approximate equivalence relation of the upper and lower approximation. Based on the optimal partition, an improved dark channel theory is presented to enhance each region of the image. After that, sonar images with different backgrounds are used to test the proposed method. The experimental results show that the gray distribution of the sonar image enhanced by this algorithm is more uniform and the boundary details are clearer. The proposed algorithm has the advantage of solving the optimal division for the set of pixels with approximate grayscale. Moreover, the proposed algorithm can get better image enhancement effect in the premise of maintaining the texture of the sonar images.

Underwater image enhancement algorithm based on dark channel prior and underwater imaging model

The main reason for the degradation of the underwater image is the light absorption and scattering. The images are captured in the underwater environment often have some problems such as loss of image information, low contrast, and color distortion. In order to solve the above problems, this paper proposes an image enhancement method for the underwater environment. With the help of the underwater imaging model and dark channel prior theory, a new idea of adding transmission correction and color compensation to G and B color channels is proposed. Experimental results show that, compared with the traditional methods, this method has a better effect on the underwater image with less color deviation.

Underwater image enhancement algorithm based on Retinex and wavelet fusion

Aiming at the problems of color cast, low contrast and fuzz in the original underwater image, this paper proposes an underwater image enhancement algorithm based on Retinex and wavelet fusion. First, this paper uses the multi-scale Retinex algorithm to perform color correction processing on underwater images. Then, this paper performs gamma transformation and sharpening on the image to improve the contrast and sharpness of the image. Finally, this paper uses wavelet fusion algorithm for image fusion to obtain underwater images with clear textures and details. Through subjective and objective evaluation with other algorithms, the algorithm proposed in this paper improves the color cast and fuzz, and improves the contrast and clarity of the image.

UCT‐GAN: underwater image colour transfer generative adversarial network

Underwater image enhancement algorithms improve image quality and indirectly enhance underwater visibility. Although many underwater image enhancement neural networks have been proposed, they require large amounts of data. To reduce the amount of data required while providing better image enhancement, this study proposes an underwater image colour transfer generative adversarial network (UCT-GAN). The authors first design a non-linear mapping function to generate colour cast images according to original images. Then, the authors utilise these image pairs (i.e. colour cast images and corresponding original images) to guide the UCT-GAN in learning the inverse function of the designed non-linear mapping function. Finally, colour cast images are restored via the inverse function. A data augmentation method based on Poisson fusion and block combination is also proposed to overcome the problem of requiring a large amount of training data. Moreover, the authors extend UCT-GAN into a multi-class colour transfer network to achieve an array of underwater image enhancements. Experimental results indicate that the proposed UCT-GAN can more effectively resolve underwater image colour cast compared to existing algorithms.

An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.