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Underwater image enhancement presents a significant challenge due to the complex and diverse underwater environments that result in severe degradation phenomena such as light absorption, scattering, and color distortion. More importantly, obtaining paired training data for these scenarios is a challenging task, which further hinders the generalization performance of enhancement models. To address these issues, we propose a novel approach, the Hybrid Contrastive Learning Regularization (HCLR-Net). Our method is built upon a distinctive hybrid contrastive learning regularization strategy that incorporates a unique methodology for constructing negative samples. This approach enables the network to develop a more robust sample distribution. Notably, we utilize non-paired data for both positive and negative samples, with negative samples are innovatively reconstructed using local patch perturbations. This strategy overcomes the constraints of relying solely on paired data, boosting the model's potential for generalization. The HCLR-Net also incorporates an Adaptive Hybrid Attention module and a Detail Repair Branch for effective feature extraction and texture detail restoration, respectively. Comprehensive experiments demonstrate the superiority of our method, which shows substantial improvements over several state-of-the-art methods in terms of quantitative metrics, significantly enhances the visual quality of underwater images, establishing its innovative and practical applicability. Our code is available at: https://github.com/zhoujingchun03/HCLR-Net. [ABSTRACT FROM AUTHOR]

Since underwater optical imaging is affected by underwater light absorption and attenuation, the pixels of images distribute unevenly, and image structures are damaged to different degrees, leading to problems such as color distortion, low contrast, and blurred details. A single-color correction method cannot improve the contrast and details of images, and a single contrast enhancement method will cause image color distortion and local contrast over-enhanced and weak-enhanced. To deal with the problems simultaneously, this paper proposes an underwater image enhancement method based on variable contrast and saturation enhancement model and local contrast correction. Firstly, we design a white balance method based on pixel transformation to adjust the pixel distribution and further work out color distortion; Moreover, a variable contrast and saturation enhancement model is designed to recover the image structure and statistical characteristics effectively and further promote global contrast; Ultimately, contrast limited histogram equalization is adopted to perform local contrast correction, and then solve the over-enhanced and weak-enhanced. The enhancement results based on the synthetic and real underwater image data sets show that the proposed method is superior to some advanced qualitative and quantitative evaluation methods and effectively elevates visual effects. [ABSTRACT FROM AUTHOR]

In complex marine environments, underwater images often suffer from color distortion, blur, and poor visibility. Existing underwater image enhancement methods predominantly rely on the U-net structure, which assigns the same weight to different resolution information. However, this approach lacks the ability to extract sufficient detailed information, resulting in problems such as blurred details and color distortion. We propose a two-branch underwater image enhancement method with an optimized original resolution information strategy to address this limitation. Our method comprises a feature enhancement subnetwork (FEnet) and an original resolution subnetwork (ORSnet). FEnet extracts multi-resolution information and utilizes an adaptive feature selection module to enhance global features in different dimensions. The enhanced features are then fed into ORSnet as complementary features, which extract local enhancement features at the original image scale to achieve semantically consistent and visually superior enhancement effects. Experimental results on the UIEB dataset demonstrate that our method achieves the best performance compared to the state-of-the-art methods. Furthermore, through comprehensive application testing, we have validated the superiority of our proposed method in feature extraction and enhancement compared to other end-to-end underwater image enhancement methods. [ABSTRACT FROM AUTHOR]

This correction notice from the International Journal of Computer Vision addresses the article "HCLR-Net: Hybrid Contrastive Learning Regularization with Locally Randomized Perturbation for Underwater Image Enhancement" by Jingchun Zhou et al. The original article was published online with Open Access under a Creative Commons Attribution license, but the authors later decided to cancel Open Access, changing the copyright to Springer Nature. The correction provides the correct DOI link for the article and acknowledges the authors involved in the research. [Extracted from the article]

The information in a single underwater image is insufficient due to the complexity of the underwater environment, which makes it challenging to meet the expectations of marine research. In this paper, we proposed a visual quality enhancement method for underwater images based on multi-feature prior fusion (MFPF), achieved by extracting and fusing multiple feature priors of underwater images. Complementary multi-features enhance the visual quality of underwater images. We designed a color correction method based on self-adaptive standard deviation, which realizes the color offset correction based on the dominant color of the underwater image. A gamma correction power function and spatial linear adjustment were also applied to achieve a set of artificial exposure map sequences obtained from a single degraded image and enhance the dark area's brightness and structural details. This design makes full use of the advantages of white balance, guided filtering, and multi-exposure sequence technology. And it uses a multi-scale fusion of various prior features to enhance underwater images. The experimental results show that by applying the multi-feature prior fusion scheme, this design comprehensively solves various degenerated problems, removes over-enhancement, and improves dark details. [ABSTRACT FROM AUTHOR]

Underwater images play an irreplaceable role as one of the carriers of underwater information acquisition. Underwater degraded images are usually affected by the color cast, noise, and blurred details, which are difficult to apply to various vision tasks. We propose a multi-scale retinex adaptive grayscale transformation underwater image enhancement method, which includes three parts: color correction, image denoising, and detail enhancement. Firstly, the multi-scale Retinex algorithm is adopted to extract the lighting components. Mean and mean square errors were introduced through linear quantization, and color recovery factors were adopted to adjust the three channels for color correction. Second, by treating the image as an anisotropic thermal field diffusing in all direction,image noise is eliminated and edge details are preserved. Finally, for different underwater degraded images, a simulated annealing optimization algorithm is introduced to perform adaptive gray-scale transformation on the image to enhance image details. The results show that the proposed method can comprehensively solve the problems of color distortion, noise, and low contrast. Compared with the state-of-the-art underwater image enhancement and restoration methods, our method has achieved better visual effects. [ABSTRACT FROM AUTHOR]

Since the attenuation and absorption of light in the water medium and the influence of the tiny suspended particles, underwater images are degraded and suffer from color cast and low contrast. An underwater image enhancement method based on color correction and three-interval histogram stretching is proposed to handle these issues. Firstly, a color correction method based on sub-interval linear transformation is designed to achieve color correction. Meanwhile, using the three adaptive sub-histograms equalization algorithm to enhance the contrast, and then the resulting images of the above-mentioned methods are combined by the multi-scale fusion. Final experiments demonstrate the proposed method can improve the image contrast, obtain the details of the bright and dark regions of the image, rich color, and finally output high-quality images. [ABSTRACT FROM AUTHOR]

To solve the color cast and low contrast of underwater images caused by the effects of light absorption and scattering, we propose a novel underwater image enhancement method via bi-interval histogram equalization. The proposed method consists of three main parts: color correction, contrast enhancement, and multiscale fusion. First, the color cast is eliminated by automatic white balancing. Then, homomorphic filtering is adopted to decompose the image into high-frequency information and low-frequency information, the high-frequency information is enhanced by the gradient field bi-interval equalization which enhances the contrast and details of the image, and the low-frequency information is disposed via gamma correction for adjusting the exposure. Finally, we adopt a multiscale fusion strategy to fuse the high-frequency information, high-frequency after bi-interval equalization, and low-frequency information based on contrast, saturation, and exposure. Qualitative and quantitative performance evaluations demonstrate that the proposed method can effectively enhance the details and global contrast of the image and achieve better exposedness of the dark areas, which outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the growing exploration of marine resources, underwater image enhancement has gained significant attention. Recent advances in convolutional neural networks (CNN) have greatly impacted underwater image enhancement techniques. However, conventional CNN-based methods typically employ a single network structure, which may compromise robustness in challenging conditions. Additionally, commonly used UNet networks generally force fusion from low to high resolution for each layer, leading to inaccurate contextual information encoding. To address these issues, we propose a novel network called Cascaded Network with Multi-level Sub-networks (CNMS), which encompasses the following key components: (a) a cascade mechanism based on local modules and global networks for extracting feature representations with richer semantics and enhanced spatial precision, (b) information exchange between different resolution streams, and (c) a triple attention module for extracting attention-based features. CNMS selectively cascades multiple sub-networks through triple attention modules to extract distinct features from underwater images, bolstering the network's robustness and improving generalization capabilities. Within the sub-network, we introduce a Multi-level Sub-network (MSN) that spans multiple resolution streams, combining contextual information from various scales while preserving the original underwater images' high-resolution spatial details. Comprehensive experiments on multiple underwater datasets demonstrate that CNMS outperforms state-of-the-art methods in image enhancement tasks. • Cascading mechanism for robust underwater image enhancement. • Multi-level sub-network delivers precise spatial representations. • Triple attention module for information distillation. [ABSTRACT FROM AUTHOR]

Due to the complex underwater environment, underwater images exhibit different degradation characteristics, severely affecting their practical applications. Although underwater image enhancement networks with physical priors exist, the statistical priors are not applicable in extreme underwater scenes. Therefore, we propose ReX-Net, a reflectance-guided underwater image enhancement network for extreme scenarios. ReX-Net leverages the complementary information of reflectance and the underwater image obtained through the original encoder and reflectance encoder to minimize the impact of different scene environments. As underwater images contain object information at different scales, the encoder includes a TriFuse reflected-image object extractor module (TRIOE), which employs Tri-scale convolutions to capture features at different scales and utilize attention mechanisms to enhance channel and spatial information. In the decoder, we design a context-sensitive multi-level integration module (CSMLI) to fuse feature vectors at different resolutions, thereby improving the expressiveness and robustness of features while avoiding artifacts and ensuring pixel accuracy. Experiments on multiple datasets demonstrate that ReX-Net outperforms existing methods. Furthermore, application experiments show the practicality of ReX-Net in other visualization tasks. • Reflectance-guided UNet for robust underwater image enhancement. • ReX-Net interactively captures complementarity from reflectance and original image. • TRIOE fuses Tri-scale and enhances features in high information density areas. • CSMLI ensures pixel accuracy and avoid artifacts, improve the multi-resolution feature. [ABSTRACT FROM AUTHOR]

Single underwater image enhancement remains a challenging ill-posed problem, even with advanced deep learning methods, due to the significant information degeneration and various irrelevant contents. Current deep learning-based underwater image enhancement methods only consider using a single clear image as a positive feature for guiding the training of the enhancement network. However, the limited amount of helpful information constrains the network performance, and irrelevant contents consume many bits. Therefore, it is crucial to efficiently utilize cross-view neighboring features and provide corresponding relevant information for underwater enhancement. To address the challenges of degraded underwater images, we propose a novel cross-domain enhancement network (CVE-Net) that uses high-efficiency feature alignment to utilize neighboring features better. We employ a self-built database to optimize the helpful information and develop a feature alignment module (FAM) to adapt the temporal features. The dual-branch attention block is designed to handle different types of information and give more weight to essential features. Experiments demonstrate that CVE-Net outperforms state-of-the-art (SOTA) underwater vision enhancement methods in terms of both qualitatively and quantitatively results, significantly boosts the performance on underwater image quality, achieving a PSNR of 28.28 dB, which is 25% higher than Ucolor on the multi-view dataset. CVE-Net improves image quality while maintaining a good complexity-performance trade-off. [ABSTRACT FROM AUTHOR]

Due to wavelength-dependent light absorption and scattering, underwater images often appear with a colour cast and blurry details. Most existing deep learning methods utilize a single input end-to-end network structure, which leads to a single form and content of the extracted features. To address these problems, we present a novel multi-feature underwater image enhancement method via embedded fusion mechanism (MFEF). We find that the quality of reconstruction results is affected by the quality of the input image to some extent, and use pre-processing to obtain high-quality images, which can improve the final reconstruction effect. We introduce the white balance (WB) algorithm and the contrast-limited adaptive histogram equalization (CLAHE) algorithm employing multiple path inputs to extract different forms of rich features in multiple views. To fully interact with features from multiple views, we design a multi-feature fusion (MFF) module to fuse derived image features. We suggest a novel pixel-weighted channel attention module (PCAM) that calibrates the detailed features of the degraded images using a weight matrix to give diverse weights to the encoded features. Ultimately, our network utilizes a fusion mechanism-based encoder and decoder that can be applied to restore various underwater scenes. In the UIEB dataset, our PSNR increased by 10.2% compared to that of Ucolor. Extensive experimental results demonstrate that the MFEF method outperforms other state-of-the-art underwater image enhancement methods in various real-world datasets. [ABSTRACT FROM AUTHOR]

Motivated by the adverse impact of light attenuation and scattering, which leads to color distortion and low contrast in underwater images, our study primarily focuses on enhancement techniques for these images using localized transmission feature analysis and global atmospheric light feature extraction. To this end, we propose a novel approach, named TANet, drawing upon the dynamics of transmission and atmospheric light. TANet integrates two primary components: a spatial domain-based Transmission-Driven Refinement module (TDR) and a frequency domain-based Atmospheric Light Removal Fourier Module (ALRF). The TDR module employs a Gated Multipurpose Unit with dual branches, selectively regulating input features. This allows for a refined merging of feature vectors that subsequently interact, enabling cross-channel feature integration. Capitalizing on the correlation between transmission and image quality, TDR facilitates the detailed enhancement of underwater images by depicting the perceived transmission across distinct image sections. Given that atmospheric light exhibits different attenuation rates under water due to varying wavelengths, and considering that atmospheric light is globally constant, thereby influencing underwater image capture, we developed the ALRF module. This caters to the processing of global information within the frequency domain, efficiently negating atmospheric light's impact on underwater images and enhancing their quality and visibility. Our TANet's superior performance is affirmed by extensive experimental results, demonstrating its effectiveness in underwater image enhancement. • UNet based on atmospheric scattering model for underwater image enhancement. • TANet interactively captures the complementarity between spatial and frequency domain. • TDR handles the impact of non-uniform transmission in the spatial domain. • ALRF uses features in frequency domain to handle global atmospheric light's impact. [ABSTRACT FROM AUTHOR]

In recent years, underwater image enhancement and restoration technologies have become increasingly important in order to optimize the efficiency of maritime operations and promote the automatic machine learning of the maritime industry. A new hierarchical attention aggregation with multi-resolution feature learning for GAN-based underwater image enhancement is proposed to address the problems of color bias, underexposure, and blurring in underwater images. The proposed method consists of a generator and a discriminator. Specifically, the generator includes an encoder, a bottleneck layer, and a decoder. Generator introduces inter-block serial connections for better adaptation to complex image scenes and task requirements, and parallel connections to extract multi-level features and enhance the expressive capacity of the network. To extract semantic and contextual information, hierarchical attention dense aggregation is designed in the encoder, which includes multi-scale feature hierarchy and dense feature hierarchy. Additionally, a multi-scale spatial attention mechanism is designed in the bottleneck layer to handle variations in underwater image scenes. In the decoder, the feature channel layer is emphasized, and a multi-channel attention mechanism is proposed to restore the multi-resolution channel features to a three-channel enhanced image. Furthermore, the angular loss function is introduced as additional supervision, which improves the similarity between the generated and original images, increases image clarity, and reduces color bias. Meanwhile, we employ the patch discriminator to enhance machine vision. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods. • HADA for semantic and contextual features using multi-scale, dense hierarchies. • Multi-scale spatial attention for global geometric structure capturing. • MCA for clear image reconstruction by using structural and contextual features. • GAN enhances machine vision and facilitate subsequent oceanic research. [ABSTRACT FROM AUTHOR]

Vision-dependent underwater vehicles are widely used in seabed resource exploration. The visual perception system of underwater vehicles relies heavily on high-quality images for its regular operation. However, underwater images taken underwater often have color distortion, blurriness, and poor contrast. To address these degradation issues, we develop an adaptive weighted multiscale retinex (AWMR) method for enhancing underwater images. To utilize the local detail features, we first divide the image into multiple sub-blocks and calculate the detail sparsity index for each one. Then, we combine the global detail sparsity index with the local detail sparsity indices to determine the optimal scale parameter and corresponding weights for each sub-block. We apply retinex processing to each sub-block using these parameters and then subject the processed sub-blocks to detail enhancement, color correction, and saturation correction. Finally, we use a gradient domain fusion method based on structure tensors to fuse the corrected and enhanced sub-blocks and obtain the final output image. Our approach improves underwater images through comparisons with current state-of-the-art (SOTA) techniques on several open-source datasets, both quality, and performance. • A new correlation coefficient is designed to balance saturation and luminance. • A dynamic parameter is suggested to improve the robustness of color correction for various scene images. • A gradient domain fusion technique utilizing structure tensors is developed to reduce the blocking artifacts. [ABSTRACT FROM AUTHOR]

• Underwater image is divided into five categories. • Calculate the color loss rate of RGB three channels. • Multi scene color restoration method to correct the color. • Stretch 64 multi contrast factor histograms to improve the contrast. Light is absorbed and scattered when propagating in water, which results in low quality and poor visibility of underwater optical images. Furthermore, the absorption of light by water causes color distortion, whereas the scattering of light by small particles suspended in water results in low image contrast. To enhance the underwater image quality, we developed a method based on light scattering characteristics. Firstly, we group the color cast into five categories according to the proportion of the average of the RGB channels. Then, we use the optical attenuation characteristics to calculate the color loss rate of the RGB channels of underwater images in different scenes and develop a multi-scene color restoration method to correct the color cast of underwater images. While keeping the color constant, we set a 64-block multi-contrast factor histogram stretching to enhance the contrast of the underwater image. The experimental results verify that the developed method has achieved image quality improvement via qualitative and quantitative evaluation. Our approach is proved to effectively improve the quality of underwater images with color distortion, low contrast, and detail loss. [Display omitted] [ABSTRACT FROM AUTHOR]

• We develop a novel encoder-decoder architecture with an attention-based skip fusion integrated into autonomous underwater robot for underwater image enhancement. • We propose a multi-scale deformable convolutional network, which acquires deformable local receptive fields at different scales and enriches the diversity of feature representation. • We adopt a parallel attention block for spatial and channel dimensions to focus on the interest regions of the input feature map. • Our MsDCANet achieves state-of-the-art performance on the recent benchmark in terms of visual effects and object metrics, which is conducive to integrating underwater robots and applications in aquaculture. Underwater images suffer from poor visibility quality, which results from selective attenuation and scattering. These interdependence phenomena together cause image degradation, failing autonomous underwater robots to recognize image contents. To address those problems, we propose a multi-scale deformable convolution network with an attention mechanism (MsDCANet) to enhance the quality of underwater images. The proposed model is generally implemented by an encoder-decoder architecture. Concretely, we first propose a multi-scale deformable convolutional network, which acquires deformable local receptive fields at different scales and enriches the diversity of feature representation. Coupled with spatial and channel attention mechanisms, the task-oriented high-level representations acquired from input feature maps, and the most significant features are highlighted. Considering the contributions of the encoder and decoder functions on the final enhancement task, an attention-based skip fusion scheme is designed to improve the original skip connection, which aims to reconstruct higher quality underwater images. Finally, the proposed model is optimized by a multi-task loss function, including pixel loss and perceptual loss. Experiments on underwater images captured under diverse scenes demonstrate that the proposed model produces visually pleasing results and has strong generalization ability, even significantly outperforming several state-of-the-art methods. Besides, our approach can also improve the performance of vision tasks and be applied to aquaculture development. [ABSTRACT FROM AUTHOR]