An Improved Underwater Image Enhancement Approach for Border Security

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

The complexity and challenging underwater environment leading to degradation in underwater image. Measuring the quality of underwater image is a significant step for the subsequent image processing step. Existing Image Quality Assessment (IQA) methods do not fully consider the characteristics of degradation in underwater images, which limits their performance in underwater image assessment. To address this problem, an Underwater IQA (UIQA) method based on color space multi-feature fusion is proposed to focus on underwater image. The proposed method converts underwater images from RGB color space to CIELab color space, which has a higher correlation to human subjective perception of underwater visual quality. The proposed method extract histogram features, morphological features, and moment statistics from luminance and color components and concatenate the features to obtain fusion features to better quantify the degradation in underwater image quality. After features extraction, support vector regression(SVR) is employed to learn the relationship between fusion features and image quality scores, and gain the quality prediction model. Experimental results on the SAUD dataset and UIED dataset show that our proposed method can perform well in underwater image quality assessment. The performance comparisons on LIVE dataset, TID2013 dataset,LIVEMD dataset,LIVEC dataset and SIQAD dataset demonstrate the applicability of the proposed method.

Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of computer vision tasks in underwater environments. Different approaches are proposed in the literature to enhance the underwater images, however the dynamically varying environmental conditions remains challenging. It requires adaptive approach to adjust the color dynamically and to estimate the features. In this study, we propose a novel hybrid approach for underwater image enhancement that integrates a customized color correction algorithm with Recurrent Convolutional Neural Networks (R-CNN), offering superior image enhancement capabilities compared to conventional methods. The proposed method comprises three key stages: color correction, recurrent feature extraction, and enhancement. The color correction algorithm goes beyond traditional method by adjusting the color channels based on dynamically estimated attenuation coefficients specific to underwater environments, effectively restoring natural color tones and also enhancing overall visual accuracy. The recurrent design enables the network to enhance feature representations by continuously refining them across iterations, thus preserving intricate scene details better than static CNNs. Finally, the novel enhancement strategy guided by the recurrently extracted features, further improves the clarity, contrast and visibility. Experimental evaluation conducted on diverse underwater image datasets demonstrate the superiority of proposed approach, offering significant improvements in image quality over state-of-the-art techniques. Qualitative and quantitative analyses reveal that the proposed method not only enhances images aesthetics but also facilitates better performance in downstream computer vision tasks such as object detection, recognition and environmental monitoring.

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.

To study the effect of decentration and tilt of the type I Boston keratoprosthesis (KPro) on image quality in both aphakic and pseudophakic eyes. An optical ray-tracing program was used to simulate the image projected onto the retina in an eye with a perfectly centered KPro, and in eyes with varying degrees of KPro decentration and tilt. Decentration was modeled along a typical white-to-white distance of 12.0 mm, and the corresponding tilt was calculated assuming a radius of curvature of 8.0 mm, the radius of curvature of the backplate of the KPro. Both aphakic and pseudophakic eyes were simulated, and the corresponding modulation transfer function curves, point spread functions, and astigmatism were analyzed. The perfectly centered KPro produced a high-quality image with no induced astigmatism. Increasing decentration beyond approximately 0.5 mm resulted in poorer image quality with a more pronounced effect in the presence of an intraocular lens. Using models of the normal eye as a threshold, image degradation due to decentration becomes clinically significant at approximately 1.4 mm and 0.9 mm for the aphakic and pseudophakic cases, respectively. Astigmatism introduced by decentration is approximately 0.25 D cylinders at those thresholds. Decentration of up to 0.5 mm had no significant impact on image quality and an attempt at good intraoperative centration of the KPro within this range is important. Conversely, decentration of 0.9 mm or more during surgical implantation can result in significant degradation in retinal image quality including astigmatism. The effect is greater in the pseudophakic eye.

Underwater images often suffer from color distortion and detail loss due to light absorption and scattering, which degrades visual quality and limits practical applications. To address these issues, a novel adaptive algorithm for underwater image color correction and detail enhancement is proposed. The algorithm first applies threshold stretching to adjust the grayscale range, enhancing contrast while mitigating the risk of localized overcompensation. Based on the color distribution, images are categorized into bluish and greenish tones, providing the foundation for the adaptive color compensation method (ACCM). The ACCM is designed to separately compensate different color channels, using the green channel as a reference to restore the most degraded channels while maintaining overall color balance. The compensation process is further constrained by the minimum color loss criterion to ensure consistent color fidelity. Furthermore, an edge detail enhancement method is formulated to recover fine details by amplifying intensity differences between the original image and its smoothed version. Extensive experiments on multiple underwater image data sets demonstrate that the proposed algorithm consistently outperforms state-of-the-art methods, achieving average improvements of 0.0021, 0.0646, 0.3677, and 0.0800 in underwater color image quality evaluation, underwater image quality metric, fog aware density evaluator, and colorfulness contrast fog density index metrics, respectively, underscoring its effectiveness and robustness across diverse underwater environments.

Captured underwater images usually suffer from severe color cast and low contrast due to wavelength-dependent light absorption and scattering. These degradation issues affect the accuracy of target detection and visual understanding. The underwater image formation model is widely used to improve the visual quality of underwater images. Accurate transmission map and background light estimation are the keys to obtaining clear images. We develop a multi-priors underwater image restoration method with adaptive transmission (MUAT). Concretely, we first propose a calculation method of the dominant channel transmission to cope with pixel interference, which combines two priors of the difference between atmospheric light and pixel values and the difference between the red channel and the blue-green channel. Besides, the attenuation ratio between the superior and inferior channels is adaptively calculated with the background light to solve the color distortion and detail blur caused by the imaging distance. Ultimately, the global white balance method is introduced to solve the color distortion. Experiments on several underwater scene images show that our method obtains accurate transmission and yields better visual results than state-of-the-art methods.

In either situation, underwater vision is affected by low light, diffraction, and color aberrations that lead to image distortion. To prevent this, we implemented a Texture Distribution Mapping Approach for undersea image improvement using MATLAB. Under this framework, we will enhance texture distribution, contrast, and visibility in general by controlling the most relevant properties of texture in the underwater environment. The TDM process is incremental: it involves pre-processing to eliminate noise and color distortion, followed by enhancement using another method for texture improvement. By enhancing texture mapping onto the image, we achieve a balance of visibility in high-texture regions, resulting in low-texture regions and better clarity and coloring. The method was compared against real underwater image data sets and tested for performance using quality measures like UCIQE (Underwater Color Image Quality Evaluation) and UIQM (Underwater Image Quality Measure). Experimental results and MATLAB implementation of the proposed algorithm exhibited stunning improvements in image quality and luminosity enhancements in texture compared to baseline methods, especially in challenging water conditions.

Unmanned underwater operations using remotely operated vehicles or unmanned surface vehicles are increasing in recent times, and this guarantees human safety and work efficiency. Optical cameras and multi-beam sonars are generally used as imaging sensors in underwater environments. However, the obtained underwater images are difficult to understand intuitively, owing to noise and distortion. In this study, we developed an optical and sonar image fusion system that integrates the color and distance information from two different images. The enhanced optical and sonar images were fused using calibrated transformation matrices, and the underwater image quality measure (UIQM) and underwater color image quality evaluation (UCIQE) were used as metrics to evaluate the performance of the proposed system. Compared with the original underwater image, image fusion increased the mean UIQM and UCIQE by 94% and 27%, respectively. The contrast-to-noise ratio was increased six times after applying the median filter and gamma correction. The fused image in sonar image coordinates showed qualitatively good spatial agreement and the average IoU was 75% between the optical and sonar pixels in the fused images. The optical-sonar fusion system will help to visualize and understand well underwater situations with color and distance information for unmanned works.

In recent days, a lot of study has been done on improving the visual quality of underwater and undersea imaging in submarine and military operations to find the hidden structure designs and sea excursions. This paper proposed an underwater image enhancement method based on colour constancy theory. When we compare with much of the existing research, the time and complexity of the proposed method are low, and it achieves excellent performance. Firstly, we analyse the underwater imaging model and distortion. Then, by compensating the red channel and applying local white balance, a linear transformation is performed. Finally, results are obtained by applying the histogram equalization to the RGB images. We also measure the image quality by taking the parameters PSNR, UIQM, UCIQE, and Entropy. These parameters are compared to proposed and existing approaches; however, our method produces higher image quality.

To evaluate metal artefact reduction (MAR) techniques from four CT vendors in hip prosthesis imaging. Bilateral hip prosthesis phantom images, obtained by using MAR algorithms for single-energy CT data or dual-energy CT (DECT) data and by monoenergetic reconstructions of DECT data, were visually graded by five radiologists using 10 image quality criteria. Comparisons between the MAR images and a reference image were performed for each scanner separately. Ordinal probit regression analysis was used. The MAR algorithms in general improved the image quality based on the majority of the criteria (up to between 8/10 and 10/10) with a statistical improvement in overall image quality (p < 0.001). However, degradation of image quality, such as new artefacts, was seen in some cases. A few monoenergetic reconstruction series improved the image quality (p < 0.004) for one of the DECT scanners, but it was only improved for some of the criteria (up to 5/10). Monoenergetic reconstructions resulted in worse image quality for the majority of the criteria (up to 7/10) for the other DECT scanner. The MAR algorithms improved the image quality of the hip prosthesis CT images. However, since additional artefacts and degradation of image quality were seen in some cases, all algorithms should be carefully evaluated for every clinical situation. Monoenergetic reconstructions were in general concluded to be insufficient for reducing metal artefacts. Qualitative evaluation of the usefulness of several MAR techniques from different vendors in CT imaging of hip prosthesis.

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.

Underwater images commonly suffer from a variety of quality degradations, such as color casts, low contrast, blurring details, and limited visibility. Existing superpixel segmentation algorithms face challenges in achieving superior performance when directly applied to underwater images with quality degradation. In this paper, to alleviate the limitations of superpixel segmentation when applied to underwater scenes, we propose the first underwater superpixel segmentation network (USNet), specifically designed according to the intrinsic characteristics of underwater images. Considering the quality degradation, we propose a multi-scale water-net module (MWM) aimed at enhancing the quality of underwater images before superpixel segmentation. The degradation-aware attention (DA) mechanism is then created and incorporated into MWM to solve light scattering and absorption, which can decrease object visibility and cause blurred edges. By effectively directing the network to prioritize locations that exhibit a considerable decrease in quality, this method enhances the visibility of those specific areas. Additionally, we extract the deep spatial features using the coordinate attention method. Finally, these features are fused with the shallow spatial information using the dynamic spatiality embedding module to embed comprehensive spatial features. Training and testing were conducted on the SUIM dataset, the underwater change detection dataset, and UIEB dataset. Experimental results show that our method achieves the best scores in terms of achievable segmentation accuracy, undersegmentation error, and boundary recall evaluation metrics compared to other methods. Both quantitative and qualitative evaluations demonstrate that our method can handle complicated underwater scenes and outperform existing state-of-the-art segmentation methods.

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.

In this paper, we propose possibility for reconstruction of surface of an underwater object or 3D scene reconstruction of an underwater environment using an economical RGB-D sensor such as Microsoft Kinect. Reconstructing the 3D surface of an underwater object is a challenging task due to degraded quality of underwater images. There are various reasons of quality degradation of underwater images i.e., non-uniform illumination of light on the surface of objects, scattering and absorption effects. Particles and impurities present in underwater produces Gaussian noise on the captured underwater optical images which degrades the quality of images. However, using depth sensors, as a cost effective alternative, we aim to show that underwater 3D scene reconstruction is possible with sight tradeoffs on accuracy but major cost saving. The acquired depth data is proposed to be processed by applying real-time mesh generating techniques from the acquired point cloud. The experimental result aims to show that the proposed method reconstructs 3D surface of underwater objects accurately using captured underwater depth images.