Underwater Image Enhancement Algorithm Research Articles

Research on multi-scale fusion image enhancement and improved YOLOv5s lightweight ROV underwater target detection method.

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields.

No-Reference Quality Assessment Based on Dual-Channel Convolutional Neural Network for Underwater Image Enhancement

Underwater images are important for underwater vision tasks, yet their quality often degrades during imaging, promoting the generation of Underwater Image Enhancement (UIE) algorithms. This paper proposes a Dual-Channel Convolutional Neural Network (DC-CNN)-based quality assessment method to evaluate the performance of different UIE algorithms. Specifically, inspired by the intrinsic image decomposition, the enhanced underwater image is decomposed into reflectance with color information and illumination with texture information based on the Retinex theory. Afterward, we design a DC-CNN with two branches to learn color and texture features from reflectance and illumination, respectively, reflecting the distortion characteristics of enhanced underwater images. To integrate the learned features, a feature fusion module and attention mechanism are conducted to align efficiently and reasonably with human visual perception characteristics. Finally, a quality regression module is used to establish the mapping relationship between the extracted features and quality scores. Experimental results on two public enhanced underwater image datasets (i.e., UIQE and SAUD) show that the proposed DC-CNN method outperforms a variety of the existing quality assessment methods.

Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment

Underwater images, as a crucial medium for storing ocean information in underwater sensors, play a vital role in various underwater tasks. However, they are prone to distortion due to the imaging environment, which leads to a decline in visual quality, which is an urgent issue for various marine vision systems to address. Therefore, it is necessary to develop underwater image enhancement (UIE) and corresponding quality assessment methods. At present, most underwater image quality assessment (UIQA) methods primarily rely on extracting handcrafted features that characterize degradation attributes, which struggle to measure complex mixed distortions and often exhibit discrepancies with human visual perception in practical applications. Furthermore, current UIQA methods lack the consideration of the perception perspective of enhanced effects. To this end, this paper employs luminance and saliency priors as critical visual information for the first time to measure the enhancement effect of global and local quality achieved by the UIE algorithms, named JLSAU. The proposed JLSAU is built upon an overall pyramid-structured backbone, supplemented by the Luminance Feature Extraction Module (LFEM) and Saliency Weight Learning Module (SWLM), which aim at obtaining perception features with luminance and saliency priors at multiple scales. The supplement of luminance priors aims to perceive visually sensitive global distortion of luminance, including histogram statistical features and grayscale features with positional information. The supplement of saliency priors aims to perceive visual information that reflects local quality variation both in spatial and channel domains. Finally, to effectively model the relationship among different levels of visual information contained in the multi-scale features, the Attention Feature Fusion Module (AFFM) is proposed. Experimental results on the public UIQE and UWIQA datasets demonstrate that the proposed JLSAU outperforms existing state-of-the-art UIQA methods.

WBi-YOLOSF: improved feature pyramid network for aquatic real-time target detection under the artificial rabbits optimization

As the pillar industry of coastal areas, aquaculture needs artificial intelligence technology to promote economic development. To realize the automation of the aquaculture industry, this paper proposes a new underwater object detection network: WBi-YOLOSF. It realizes the automatic classification and detection of aquatic products, improves the production efficiency of the aquaculture industry, and promotes its economic development. This paper creates an image dataset containing 15 aquatic products to lay the data foundation for model training. In the data preprocessing part, an underwater image enhancement algorithm is proposed to improve the quality of the data set effectively. Aiming at the problem of high false detection rate and missed detection rate of underwater dense small targets, a new data enhancement method was proposed to improve the training set’s data quality comprehensively. Inspired by the weighted bidirectional feature pyramid network, this paper proposes a new feature extraction network: AU-BiFPN, which solves the gradient problem caused by the network hierarchy’s deepening on enhancing the network’s multi-scale feature fusion. The AU-BiFPN network structure is embedded into the YOLO series network framework, significantly improving the basic network’s feature extraction and feature fusion ability and dramatically improving the prediction accuracy without affecting the network inference speed. Here, the swarm intelligence algorithm is introduced to optimize the model hyperparameters, accelerating the convergence speed of model training and significantly reducing the computational cost. At the same time, the model’s accuracy is improved by a cliff. In addition, the Funnel Activation is introduced in the network’s backbone, and the simple, parameter-free attention module is integrated, effectively improving the accuracy and speed of the model prediction. Ablation and comparison experiments show the effectiveness and superiority of the proposed model. Verified by the mean average precision and frame rate evaluation indicators, the experimental results of the WBi-YOLOSF target detection network can reach 0.982 and 203 frames per second, which are 1.4% and five frames per second higher than the network with the second score. In summary, this method can quickly and accurately identify aquatic products, realize real-time target detection of aquatic products, and lay the foundation for developing an aquaculture automation system.

Retinex-based underwater image enhancement via adaptive color correction and hierarchical U-shape transformer.

Underwater images can suffer from visibility and quality degradation due to the attenuation of propagated light and other factors unique to the underwater setting. While Retinex-based approaches have shown to be effective in enhancing the underwater image quality, the use of hand-crafted priors and optimization-driven solutions often prevent the adaptivity of these methods to different types of underwater images. Moreover, the commonly-used white balance strategy which often appears in the preprocessing stage of the underwater image enhancement (UIE) algorithms may give rise to unwanted color distortions due to the fact that wavelength-dependent light absorption is not taken into account. To overcome these potential limitations, in this paper, we present an effective UIE model based on adaptive color correction and data-driven Retinex decomposition. Specifically, an adaptive color balance approach which takes into account different attenuation levels for light with different wavelengths is proposed to adaptively enhance the three color channels. Furthermore, deep neural networks are employed for the Retinex decomposition, formulating the optimization problem as an implicit-prior-regularized model which is solved by learning the priors from a large training dataset. Finally, a hierarchical U-shape Transformer network which uses hierarchically-structured multi-scale feature extraction and selective feature aggregation is applied to the decomposed images for contrast enhancement and blur reduction. Experimental results tested on six benchmark underwater image datasets demonstrate the effectiveness of the proposed UIE model.

Underwater Image Enhancement Algorithm Based on Adversarial Training

Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications.

Hierarchical prior-guided quality assessment method for underwater images

Many underwater image enhancement (UIE) algorithms have been proposed to improve the quality of the underwater image. However, the lack of effective objective image quality assessment (IQA) methods has hindered the further development of UIE algorithms. Unlike traditional distorted images, underwater images usually contain natural distortions such as color cast and fog effects induced by light absorption and scattering. In addition, due to the complexity of underwater scenes, the enhanced results generated using existing UIE algorithms may have artificial distortions such as under-enhancement, over-enhancement, and detail loss. Thus, it is difficult for traditional IQA methods to comprehensively capture quality-related features in complicated underwater conditions. Based on this, this paper proposes a new hierarchical prior-guided quality assessment method for underwater images with the aid of deep learning technology, which first decomposes the underwater image into the base layer and detail layer. Then, the two layers are taken as input of feature extraction network for extracting the luminance information and texture information of the image, respectively. Meanwhile, two pseudo-reference images are used to guide model training so as to obtain luminance and texture features more accurately and efficiently. Finally, the two types of features are fused to obtain the quality score of the underwater image. Extensive experiments have been conducted on two publicly available underwater image datasets, and the experimental results show that the proposed method outperforms other underwater image quality assessment methods.

YOLO‐UOD: An underwater small object detector via improved efficient layer aggregation network

AbstractAccurate detection of underwater objects is a key indicator technology to effectively enhance the field of marine development and application, and is of great importance to various fields including marine military defense and seafood aquaculture. Efficient and rapid detection of underwater targets is a crucial technological challenge in this field. To meet the challenges posed by these issues, this study applies the convolutional omni‐efficient layer aggregation network (CO‐ELAN) module to the detector backbone to improve the ability of the network structure to acquire underwater objects from image information. The module improves the feature representation of gradient branching through a multi‐dimensional dynamic convolution and attention mechanism. In terms of loss calculation, the optimized normalized Wasserstein distance approach is used to predict the box distribution probabilistic modelling method to determine comparable distances to the ground box and obtain better samples of small target labels. Here, an underwater image enhancement algorithm based on white balance and underwater blur fusion is used to obtain clear images that enable improved detector performance. After the verification experiment on the URPC2018 dataset, it is found that the detector has better underwater detection ability compared with other detectors in the complex underwater environment. The proposed method achieves a 2.4% improvement over the YOLOv7 baseline model, while reducing computation costs by 5%.

Research on Underwater Image Enhancement Algorithm

In recent years, with the continuous expansion of human activity areas, the Marine pollution caused by human activities has become increasingly serious, and the large influx of agricultural emissions and household garbage needs to rely on underwater equipment to clean up. However, to identify garbage from the complex underwater environment, it is necessary to study the underwater image enhancement algorithm. Therefore, this paper takes underwater plastic floaters as the research object, starts with the optical imaging model of underwater images, analyzes the process and mechanism of underwater optical imaging, and its impact on image quality, so as to better understand the causes of image degradation. Based on white balance algorithm, histogram equalization algorithm, bilateral filter algorithm and gamma correction method, the underwater image recognition image is improved. The principle of these methods to enhance underwater image is analyzed and applied to further improve the effect of underwater image enhancement.

WaterPairs: a paired dataset for underwater image enhancement and underwater object detection

Due to its importance in marine engineering and aquatic robotics, underwater image enhancement works as a preprocessing step to improve the performance of high-level vision tasks such as underwater object detection and recognition. Although several studies exhibit that underwater image enhancement algorithms can boost the detection accuracy of detectors, no work has focused on studying the relationship between these two tasks. This is mainly because current underwater datasets lack either bounding box annotations or high-quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To examine how underwater image enhancement methods affect underwater object detection tasks, we provide a large-scale underwater object detection dataset with both bounding box annotations and high-quality reference images, namely, the WaterPairs dataset. The WaterPairs dataset offers a platform for researchers to comprehensively study the influence of underwater image enhancement algorithms on underwater object detection tasks. We will release our dataset at https://github.com/IanDragon/WaterPairs once this paper is accepted.

Instance Segmentation of Underwater Images by Using Deep Learning

Based on deep learning, an underwater image instance segmentation method is proposed. Firstly, in view of the scarcity of underwater related data sets, the size of the data set is expanded by measures including image rotation and flipping, and image generation by a generative adversarial network (GAN). Next, the underwater image data set is finally constructed by manual labeling. Then, in order to solve the problems of color shift, blur and the poor contrast of optical images caused by the complex underwater environment and the attenuation and scattering of light, an underwater image enhancement algorithm is used to first preprocess the data set, and several algorithms are discussed, including multi-scale Retinex (MSRCR) with color recovery, integrated color model (ICM), relative global histogram stretching (RGHS) and unsupervised color correction (UCM), as well as the color shift removal proposed in this work. Specifically, the results indicate that the proposed method can largely increase the segmentation mAP (mean average precision) by 85.7% compared with without the pretreatment method. In addition, based on the characteristics of the constructed underwater dataset, the feature pyramid network (FPN) is improved to some extent, and the preprocessing method is further combined with the improved network for experiments and compared with other neural networks to verify the effectiveness of the proposed method, thus achieving the effect and purpose of improving underwater image instance segmentation and target recognition. The experimental analysis results show that the proposed model can achieve a mAP of 0.245, which is about 1.1 times higher than other target recognition models.

Marine zoobenthos recognition algorithm based on improved lightweight YOLOv5

Detecting the distribution and density of marine zoobenthos is crucial for monitoring healthy coastal ecosystems and for growth reference tracking in precision aquaculture. However, current detection algorithms for marine zoobenthos have high computational complexity and cannot guarantee a balance between accuracy and speed, limiting their deployment in fishery equipment. This study used a portion of the Augmented Underwater Detection Dataset, a large underwater biological dataset containing marine zoobenthos data. A marine zoobenthos recognition algorithm was proposed for sea cucumbers, sea urchins, and scallops based on an improved lightweight YOLOv5, which can recognize the three types of marine zoobenthos. In the image enhancement module, an underwater image enhancement algorithm based on color balance and multi-input fusion is used, which turns the blurred image into a natural appearance of the seabed image. The lightweight backbone network EfficientnetV2-S was chosen to replace the original YOLOv5 backbone network, reducing network parameter calculations and improving recognition speed. A Bottleneck Transformer was introduced into the backbone network, and an attention mechanism based on the convolution module was introduced to construct the embedded Convolutional Block Attention Module in the Neck structure of YOLOv5, thereby improving the recognition accuracy of the lightweight YOLOv5 model. The experimental results showed that the mAP of the proposed algorithm reached 0.941, which is an improvement of 0.002 compared with the original YOLOv5l algorithm. The computation of this algorithm is 37.0 FLOPs (G), the model size is 54 MB, and the inference time is 5.9 ms. Compared to the original YOLOv5l algorithm, the reductions are 66.1%, 40.5%, and 39.2%. The proposed algorithm efficiently identified and classified marine zoobenthos.

Channel adaptive underwater image enhancement algorithm based on pixel level

Channel adaptive underwater image enhancement algorithm based on pixel level

GCCF: A lightweight and scalable network for underwater image enhancement

Underwater images suffer from color deviation and low contrast arising from light attenuation and scatter, making them act as a drag on exploring and exploiting ocean resources. Existed underwater image enhancement algorithms are either inflexible due to scene-dependent design or redundant for ignoring prior knowledge. To address the above problems, we propose a lightweight and scalable deep learning-based network that consists of a Grouped Color Compensation encoder and channel Fusion decoder, abbreviated GCCF. Concretely, the least absorbed green channel contains abundant information compared to the red and blue channels. Taking this into account, the encoder extracts information independently from three grouped features according to the color. Simultaneously, the encoder flexibly compensates for the red and blue channels from the green channel with a proposed learnable module. Then, the decoder gradually fuses the grouped features into a whole part and produces competitive outputs from the integrated features. Extensive experiments on real-world underwater images demonstrate that the proposed GCCF achieves outstanding results in subjective visual comparisons and objective metrics.

PRIDNet based Image Denoising for Underwater Images

Underwater image enhancement has become a popular research topic due to its importance in aquatic robotics and marine engineering. However, the underwater images frequently experience signal-dependent speckle noise when transmitting and acquiring data, which can limit certain applications such as detection, object tracking. In the recent years, the existing underwater image enhancement algorithms efficiency has been analysed and evaluated on a small number of carefully chosen real-world images or synthetic datasets. As such, it is challenging to predict how these algorithms might function with images acquired in the wild under various circumstances. This paper introduces a new solution for noise removal from underwater images called Pyramid Real Image Noise Removal Network (PRIDNet) with patches.PRIDNet is a three-level network design using image patches. The tests were carried out on a dataset of actual noisy images demonstrate that, in terms of quantitative metrics, our proposed denoising model reduction performs better with the exixting denoisers. We determine the effectiveness and constraints of existing algorithms using benchmark assessments and the suggested model, offering valuable information for further studies on underwater image enhancement.

Underwater image enhancement algorithm based on color correction and contrast enhancement

Underwater image enhancement algorithm based on color correction and contrast enhancement

Shallow Marine High-Resolution Optical Mosaics Based on Underwater Scooter-Borne Camera.

Optical cameras equipped with an underwater scooter can perform efficient shallow marine mapping. In this paper, an underwater image stitching method is proposed for detailed large scene awareness based on a scooter-borne camera, including preprocessing, image registration and post-processing. An underwater image enhancement algorithm based on the inherent underwater optical attenuation characteristics and dark channel prior algorithm is presented to improve underwater feature matching. Furthermore, an optimal seam algorithm is utilized to generate a shape-preserving seam-line in the superpixel-restricted area. The experimental results show the effectiveness of the proposed method for different underwater environments and the ability to generate natural underwater mosaics with few artifacts or visible seams.

Deep Neural Network model for Underwater Image Enhancement

In recent years, there has been a growing interest in the field of underwater image enhancement, driven by its significance in underwater robotics and ocean engineering. Initially, research efforts focused on physics-based approaches, but with advancements in technology, the utilization of deep convolutional neural networks (CNNs) and generative adversarial networks (GANs) has become prevalent. These state-of-the-art algorithms have shown impressive results; however, their computational complexity and memory requirements pose challenges to their practical implementation on portable devices used for underwater exploration tasks. Furthermore, these models are often trained on either synthetic or limited real-world datasets, limiting their applicability in real-world scenarios. In this paper, we propose a novel deep neural network architecture that maintains high performance while reducing the number of parameters compared to existing state-of-the-art models. Our approach aims to address the computational and memory limitations associated with underwater image enhancement algorithms. By leveraging the strengths of our architecture, we demonstrate its generalization capability by evaluating its performance on a combination of synthetic and real-world datasets. This approach enhances the practicality and applicability of our model in real-world underwater scenarios.

Joint polarization detection and degradation mechanisms for underwater image enhancement.

Light absorption and scattering exist in the underwater environment, which can lead to blurring, reduced brightness, and color distortion in underwater images. Polarized images have the advantages of eliminating underwater scattering interference, enhancing contrast, and detecting material information of the object in underwater detection. In this paper, from the perspective of polarization imaging, different concentrations (0.15g/ml, 0.30g/ml, and 0.50g/ml), different wave bands (red, green, and blue), different materials (copper, wood, high-density PVC, aluminum, cloth, foam, cloth sheet, low-density PVC, rubber, and porcelain tile), and different depths (10cm, 20cm, 30cm, and 40cm) are set up in a chamber for the experimental environment. By combining the degradation mechanism of underwater images and the analysis of polarization detection results, it is proved that the degree of polarization images have greater advantages than degree of linear polarization images, degree of circular polarization images, S1, S2, and S3 images, and visible images underwater. Finally, a fusion algorithm of underwater visible images and polarization images based on compressed sensing is proposed to enhance underwater degraded images. To improve the quality of fused images, we introduce orthogonal matching pursuit (OMP) in the high-frequency part to improve image sparsity and consistency detection in the low-frequency part to improve the image mutation phenomenon. The fusion results show that the peak SNR values of the fusion result maps using OMP in this paper are improved by 32.19% and 22.14% on average over those using backpropagation and subspace pursuit methods. With different materials and concentrations, the underwater image enhancement algorithm proposed in this paper improves information entropy, average gradient, and standard deviation by 7.76%, 18.12%, and 40.8%, respectively, on average over previous algorithms. The image NIQE value shows that the image quality obtained by this paper's algorithm is improved by about 69.26% over the original S0 image.

Real-time jellyfish classification and detection algorithm based on improved YOLOv4-tiny and improved underwater image enhancement algorithm

The outbreak of jellyfish blooms poses a serious threat to human life and marine ecology. Therefore, jellyfish detection techniques have earned great interest. This paper investigates the jellyfish detection and classification algorithm based on optical images and deep learning theory. Firstly, we create a dataset comprising 11,926 images. A MSRCR underwater image enhancement algorithm with fusion is proposed. Finally, an improved YOLOv4-tiny algorithm is proposed by incorporating a CBMA module and optimizing the training method. The results demonstrate that the detection accuracy of the improved algorithm can reach 95.01%, the detection speed is 223FPS, both of which are better than the compared algorithms such as YOLOV4. In summary, our method can accurately and quickly detect jellyfish. The research in this paper lays the foundation for the development of an underwater jellyfish real-time monitoring system.