Multi-prior underwater image restoration method via adaptive transmission.

Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of human visual systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.

In recent years, enhancement of underwater images based on generative adversarial network (GAN) has been widely used. Aiming at the shortcoming that the texture details of the images generated by GAN are not clear enough, an underwater image pre-process unit and a generator including a perceptual subnet and a refine subnet are designed to improve visual quality of underwater images. The role of the perceptual subnet is to maintain the structure and semantic information of the input image. Refine subnet is to make the generated image clearer in the details of the texture. Specially, considering that underwater images may prevent the network from learning effectively, an underwater image pre-process unit is proposed to improve the quality of underwater images. To maintain the structure and semantic information of underwater images and preserve texture details of underwater images, the perceptual subnet and the refine subnet are proposed, respectively. Then, color-structure perception loss is proposed to obtain good performance in color and structure, and content loss and detail loss are proposed to keep content consistent and sharper texture. Ablation study verify the rationality and effectiveness of each loss function. Finally, subjective and objective experiments prove that the proposed method can obtain underwater images with higher visual quality and clearer texture details.

Underwater optical imaging is essential for exploring the underwater environment to provide information for planning and regulating underwater activities in various underwater applications, such as aquaculture farm observation, underwater topographical survey, and underwater infrastructure monitoring. Thus, there is a need to investigate the underwater imaging process and propose clear and long-range underwater optical imaging methods to fulfill the demands of academia and industry. In this manuscript, we classify the eighteen most commonly used underwater optical imaging methods into two groups regarding the imaging principle, (1) hardware and (2) software-based methods, each with an explanation of the theory, features, and applications. Furthermore, we also discuss the current challenges and future directions for improving the performance of current methods, such as improving the accuracy of underwater image formation model estimation, enlarging the underwater image dataset, proposing comprehensive underwater imaging evaluation metrics, estimating underwater depth and integrating different methods (e.g., hardware- and software-based methods for computational imaging) to promote the imaging performance not only in the laboratory but also in practical underwater scenarios.

Underwater polarimetric imaging for visibility enhancement utilizing active unpolarized illumination

The attenuation and scattering of different colors of light underwater are wavelength- and distance-dependent, leading to various degradation problems in underwater images. When enhancing underwater images, many deep learning-based methods rely solely on convolutional neural networks to learn a mapping from degraded images to clear images to achieve enhanced effects. However, such methods have limitations in capturing long-term dependencies, preventing them from accurately capturing the global information of images. Although Transformers can solve this problem, there is a lack of inductive bias in training due to the limited number of training datasets with certain degradation phenomena. To address this issue, a novel Swin Transformer based on physical perception is proposed for the first time. Swin Transformer is used to solve the long- and short-distance dependency problem. Additionally, the underwater image degradation process is considered in network design to solve the problem of poor inductive bias. Combining the advantages of physical imaging, convolutional neural networks and Transformer can effectively improve the visual quality of underwater images. Rich qualitative and quantitative experimental results show that our Transformer achieves competitive performance on 5 benchmark datasets.

Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method.

An efficient nonlocal variational method with application to underwater image restoration

A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement.

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.

Owing to absorption and scattering of light in water, raw underwater images suffer from low contrast, blurred details, and color casts. These characteristics significantly interfere the visibility of underwater images. In this paper, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptive mechanism for underwater image enhancement. A new method for simulating underwater-like training dataset by underwater image formation model is proposed. Upon this, a novel framework, which introduces a domain adaptive mechanism and a physics model constraint feedback control, is trained to enhance the underwater scenes. Extensive experiments demonstrate the significant superiority of the proposed framework compared with the state-of-the-art methods.

Underwater photography is important for the works of exploring underwater scenes. Comparing with normal camera images, the underwater images usually have low brightness-contrast and with low visual quality. This paper proposes a new method to improve the underwater image visual quality by using a two-step approach. Firstly, a transmission map based enhancement is applied to increase the image global contrast, similar to the image defog algorithm. Secondly, image details are extracted and the local contrast is improved by applying edge-preserving filter. Experiments demonstrated that the proposed two-step approach can significantly improve the visual quality of underwater images.

In response to the challenges of blur distortion, low contrast and color fading in underwater images, caused by complex environmental factors and light attenuation, this study presents a novel underwater image enhancement method that leverages the U-Net architecture and channel attention mechanism fusion generative adversarial network (GAN), named UAEGAN. UAEGAN is built on the framework of GAN, combining the U-Net structure with a channel attention mechanism to construct a generator network, reducing the loss of low-level information during feature extraction and enhancing image details. Additionally, the algorithm employs a PatchGAN discriminator, which improves image resolution and detail representation by performing fine-grained true/false judgments on local image patches. Finally, the visual quality of the enhanced image is further optimized through the weighted fusion of multiple loss functions. Experimental results on the UIEB dataset indicate that UAEGAN outperforms the latest methods in terms of both visual quality and numerical metrics. The algorithm effectively enhances the clarity and visual quality of underwater images, providing strong support for subsequent underwater image processing tasks and applications.

Underwater imaging is often affected by light scattering, which leads to a significant degradation in image quality and resolution. We propose a digital holographic microscopy method with polarization multiplexing for underwater imaging and descattering. Our method is based on a single laser source, so that the reference light in different polarization states can interfere with the object light at the same time, and the required information can be extracted in the frequency spectrum. Combined with the underwater image formation model, we can achieve descattering in one step. The experimental results demonstrate the feasibility of the approach as a leading example combining polarization imaging and digital holography. This method will benefit the underwater image descattering for visualizing microplastics and other in situ imaging situations.

Contour and texture preservation underwater image restoration via low-rank regularizations

The underwater visibility is very important in underwater vision research and target detection. However, most underwater vision systems cannot guarantee to possess the performance under complex water conditions. This is because underwater images are usually degraded by light-water interactions of absorption and scattering. The ambient light is scattered into the cameras line of sight by water molecules and suspended particles in the water medium, which adds a layer of haze to the image and reduces the contrast of the image. This part of scattered light is usually called background light, which is the main reason for underwater image degradation. In this paper, the formations of background light in underwater imaging under two different lighting conditions: natural illumination and artificial lighting, are analyzed by setting up physical models. The models developed include the parameters such as camera parameters, light source parameters, inherent optical properties, and camera-source-object geometry. Based on the models, the relationship between the background light and the above parameters is studied. Computer analysis shows that the global background light under two illumination conditions has a close relationship between the inherent optical properties of water medium and camera parameters. The global background light under natural illumination is proportional to the scattering coefficient and inversely proportional to the attenuation coefficient. The background light under the two illumination conditions both can be described in simple exponential falloff expressions of the global background light. The simple expression greatly reduces the computational complexity of simulations. The intensity of background light mainly depends on the inherent optical properties, camera-scene distance, camera-source distance and cameras imaging angle. The relationship between the global background light and the inherent optical properties can be used to estimate the attenuation coefficient, scattering coefficient and scene depth information. The result of this paper can be very useful for designing and improving the underwater imaging systems.