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The underwater environment is characterized by complex light traversal, encompassing effects such as color loss, contrast loss, water distortion, backscatter, light attenuation, and color cast, which vary depending on water purity, depth, and other factors. The dataset presented in this paper is prepared with 100 ground-truth images and 1,50,000 synthetic underwater images. This dataset approximates the effects of underwater environment with implementable combinations of color cast, blurring, low-light, and contrast reduction. These effects and their combinations, with different severity levels are applied to each ground-truth image to generate as many as 150 synthetic underwater images. In addition to the dataset of 1,50,100 images, a comprehensive set of 21 focus metrics, including the average contrast measure operator, Brenner's gradient-based metric, and many others, are calculated for each image.

Images captured in hazy scenes exhibit severe degradation. Various dehazing methods have been proposed recently. However, most of them have the drawback of color cast in the dehazing results. To solve this problem, we proposed an image dehazing algorithm called IDACC that avoids color cast by using a novel atmospheric scattering model (NASM). The NASM is obtained by introducing a scattering compensation coefficient into the atmospheric light term of the traditional atmospheric scattering model (ASM). The scattering compensation coefficient effectively suppresses the color cast caused by the atmospheric light term. Moreover, we designed a global information search (GIS) strategy based on the minimum value channel to facilitate the calculation of the transmission map. Extensive experiments show that IDACC achieves image dehazing and effectively avoids color cast. Note that the IDACC algorithm does not require to consume time in dataset collection and model training. Furthermore, IDACC needs to only input a hazy image and can directly output a haze-free image. Besides, our method shows excellent performance both quantitatively and qualitatively compared with state-of-the-art (SOTA) methods.

The domain of underwater imaging is riddled with multifarious challenges, such as light attenuation, scattering, and color distortion, which can have a detrimental impact on the quality of images. In order to address these challenges, the Generalized Color Compensation (GCC) technique has been introduced, which utilizes color compensation and color mean adjustment to rectify color cast while integrating contrast enhancement via the Contrast Limited Adaptive Histogram Equalization (CLAHE). Nevertheless, the performance of GCC is limited due to the production of bright and smooth images. To overcome this challenge, we have introduced the improved GCC approach, which employs color compensation and color mean adjustment to rectify color cast. Subsequently, a contrast-enhanced image is generated through CLAHE to improve image contrast, while the detail-enhanced image is produced via a cumulative distribution function. Furthermore, image fusion between the detail-enhanced and contrast-enhanced images yields a superior-quality image. Our experimental results demonstrate the effectiveness of our proposed technique in improving the visual quality of underwater images. Objective metrics such as Underwater Image Quality Measure (UIQM) demonstrate that our technique surpasses GCC in terms of image sharpness, colorfulness, and contrast. [ABSTRACT FROM AUTHOR]

Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, various color correction-based dehazing algorithms have recently been implemented to restore colorcast images. However, existing color restoration studies are limited in that they struggle to distinguish between haze and objects, particularly when haze veils and images have a similar color or when objects with a high saturation value occupy a significant portion of the scene, resulting in overly grayish images and distorted colors. Therefore, we propose a saturation-based dehazing method that extracts only the hue of the cast airlight and preserves the information of the object. First, the proposed color correction method uses a dominant color extraction method for the clustering of CIELAB(LAB) color images and then assigns area scores to the classified clusters. Sorting of the airlight areas is performed using the area score, and gray world-based white balance is performed by extracting the hue of the area. Finally, the saturation of the restored image is used to separate and process the distant objects and airlight, and dehazing is performed by applying a weighting value to the depth map based on the average luminance. Our color restoration method prevents excessive gray tone and color distortion. In particular, the proposed dehazing method improves upon existing issues where near-field information is lost and noise is introduced in the far field as visibility improves. [ABSTRACT FROM AUTHOR]

Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, various color correction-based dehazing algorithms have recently been implemented to restore colorcast images. However, existing color restoration studies are limited in that they struggle to distinguish between haze and objects, particularly when haze veils and images have a similar color or when objects with a high saturation value occupy a significant portion of the scene, resulting in overly grayish images and distorted colors. Therefore, we propose a saturation-based dehazing method that extracts only the hue of the cast airlight and preserves the information of the object. First, the proposed color correction method uses a dominant color extraction method for the clustering of CIELAB(LAB) color images and then assigns area scores to the classified clusters. Sorting of the airlight areas is performed using the area score, and gray world-based white balance is performed by extracting the hue of the area. Finally, the saturation of the restored image is used to separate and process the distant objects and airlight, and dehazing is performed by applying a weighting value to the depth map based on the average luminance. Our color restoration method prevents excessive gray tone and color distortion. In particular, the proposed dehazing method improves upon existing issues where near-field information is lost and noise is introduced in the far field as visibility improves.

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The underwater environment is characterized by complex light traversal, encompassing effects such as color loss, contrast loss, water distortion, backscatter, light attenuation, and color cast, which vary depending on water purity, depth, and other factors. The dataset presented in this paper is prepared with 100 ground-truth images and 1,50,000 synthetic underwater images. This dataset approximates the effects of underwater environment with implementable combinations of color cast, blurring, low-light, and contrast reduction. These effects and their combinations, with different severity levels are applied to each ground-truth image to generate as many as 150 synthetic underwater images. In addition to the dataset of 1,50,100 images, a comprehensive set of 21 focus metrics, including the average contrast measure operator, Brenner's gradient-based metric, and many others, are calculated for each image., (© 2024 The Author(s).)

Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years. These algorithms developed upon various assumptions demonstrate successes from various aspects using different data sets and different metrics. In this work, we setup an undersea image capturing system, and construct a large-scale Real-world Underwater Image Enhancement (RUIE) data set divided into three subsets. The three subsets target at three challenging aspects for enhancement, i.e., image visibility quality, color casts, and higher-level detection/classification, respectively. We conduct extensive and systematic experiments on RUIE to evaluate the effectiveness and limitations of various algorithms to enhance visibility and correct color casts on images with hierarchical categories of degradation. Moreover, underwater image enhancement in practice usually serves as a preprocessing step for mid-level and high-level vision tasks. We thus exploit the object detection performance on enhanced images as a brand new task-specific evaluation criterion. The findings from these evaluations not only confirm what is commonly believed, but also suggest promising solutions and new directions for visibility enhancement, color correction, and object detection on real-world underwater images. The benchmark is available at: https://github.com/dlut-dimt/Realworld-Underwater-Image-Enhancement-RUIE-Benchmark. [ABSTRACT FROM AUTHOR]

Featured Application: This work aims for recovering realistic colors of underwater images, which is very important for the detection and classification of underwater targets. Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes. [ABSTRACT FROM AUTHOR]

ED light, a green energy-saving light source, can cause color cast. For this reason, LED light is seldom favored by designers. The purpose of the paper is to provide shoppers who are observing product colors in an LED-lighted setting with an innovative color identification model. Based on designers’ product color comparison, the paper employs high-reliability mechanic visual perception in combination with grey relational grade. Grey relational grade is applied to eliminate electrical fault pertaining to mechanic visual perception, whereby appropriate LED parameters and color cast inclination can be obtained. The paper first mimics retail store display windows. The color temperature and illuminance of LED light sources are adjustable. Two degrees of illuminance, including high illuminance (1500 lux) and low illuminance (500 lux), and two light source color temperatures, including yellow light (2700 K) and white light (4000 K), were assigned for study. Four colors, including red, yellow, blue and green of the natural color system, were selected as product colors. The mechanic visual perception sensor was used to identify the object (product) color, which is then converted into an RGB color model to serve as research data of color cast measurement, and the grey relational grade was applied to obtain the most appropriate LED light parameters and the color cast of the four colors. The data analysis reveals that green shows the least color cast when it is lighted by a yellow LED light source with low illuminance, yellow and blue have the least color cast when it is lighted by a white LED light source with high illuminance and red displays the least color cast when it is lighted by a white LED light source with low-illuminance. The analysis also indicates each color’s cast inclination in blackness, chromaticness and hue. As a result, LED light that is more acceptable to designers is suggested for display windows, thus reducing problems with product color cast.

The low power consumption of RLCDs has significant advantages over traditional display solutions, making it a competitive candidate for next‐generation green displays. However, in some cases, improper design may cause the RLCD to turn yellowish, causing display distortion. To solve this, we proposed a series of methods to solve such color cast problems. In this paper, the reflectance associated with the phase state between the circular polarizer and the liquid crystal has been adjusted by liquid crystal to decrease this color cast. By redesigning the liquid crystal parameters, the color deviation has been successfully improved. Finally, the samples produced on production lines verified that the Improvement Scheme did make the yellowish panel whiter. [ABSTRACT FROM AUTHOR]

Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes.

In this paper, we present an optimal image enhancement technique for color cast images by preserving their intensity. There are methods which improves the appearance of the affected images under different cast like red, green, blue etc but up to some extent. The proposed color cast method is corrected by using transformation function based on gamma values. These optimal values of gamma are obtained through particle swarm optimization (PSO). This technique preserves the image intensity and maintains the originality of color by satisfying the modified gray world assumptions. For the performance analysis, the image distance metric criteria of CIELAB color space is used. The effectiveness of the proposed approach is illustrated by testing the proposed method on color cast images. It has been found that distance between the reference image and the corrected proposed image is negligible. The calculated value of image distance depicts that the enhanced image results of the proposed algorithm are closer to the reference images in comparison with other existing methods. [ABSTRACT FROM AUTHOR]

Aimed at the two problems of underwater imaging, fog effect and color cast, an Improved Segmentation Dark Channel Prior (ISDCP) defogging method is proposed to solve the fog effects caused by physical properties of water. Due to mass refraction of light in the process of underwater imaging, fog effects would lead to image blurring. And color cast is closely related to different degree of attenuation while light with different wavelengths is traveling in water. The proposed method here integrates the ISDCP and quantitative histogram stretching techniques into the image enhancement procedure. Firstly, the threshold value is set during the refinement process of the transmission maps to identify the original mismatching, and to conduct the differentiated defogging process further. Secondly, a method of judging the propagating distance of light is adopted to get the attenuation degree of energy during the propagation underwater. Finally, the image histogram is stretched quantitatively in Red-Green-Blue channel respectively according to the degree of attenuation in each color channel. The proposed method ISDCP can reduce the computational complexity and improve the efficiency in terms of defogging effect to meet the real-time requirements. Qualitative and quantitative comparison for several different underwater scenes reveals that the proposed method can significantly improve the visibility compared with previous methods. [ABSTRACT FROM AUTHOR]

In other way misuse of correct illumination at the capture moment could affect the image landmarks ; regarding color brightness and the increasing “color cast “ which might cause the image to appear in an unacceptable Or unexpected manner. Thus; several algorithms have been developed to solve these problems and balancing image color and recover the real color of the landscape. In this research an algorithm has been developed, depending on some statistics tools like (Mean, Variance and Equivalent Circle). Which leads to finding out the influential color in the image which leads to the alteration of the nature of its colors. It is called “color cast “. It could be classified into evident cast, predominant color, ambiguous cast or no cast. Then removing the cast distortion from the image and using error back propagation network for images classification into color cast carrier or uncarrier. This research has been applied on colored digital photos (BMP). More than (100) colored images were also used containing all sorts of color cast that will be found out, classified and finally removed from the image by using algorithm. The percentage of images which have no cast are (27%),The images have evident cast are (25%), where the images which have ambiguous cast are (16%),At the last ;the images which classified as predominant color are (12%),as well as there are (20%) of images classified as wrong .

An RGB YCbCr Processing method (RYPro) is proposed for underwater images commonly suffering from low contrast and poor color quality. The degradation in image quality may be attributed to absorption and backscattering of light by suspended underwater particles. Moreover, as the depth increases, different colors are absorbed by the surrounding medium depending on the wavelengths. In particular, blue/green color is dominant in the underwater ambience which is known as color cast. For further processing of the image, enhancement remains an essential preprocessing operation. Color equalization is a widely adopted approach for underwater image enhancement. Traditional methods normally involve blind color equalization for enhancing the image under test. In the present work, processing sequence of the proposed method includes noise removal using linear and non-linear filters followed by adaptive contrast correction in the RGB and YCbCr color planes. Performance of the proposed method is evaluated and compared with three golden methods, namely, Gray World (GW), White Patch (WP), Adobe Photoshop Equalization (APE) and a recently developed method entitled "Unsupervised Color Correction Method (UCM)". In view of its simplicity and computational ease, the proposed method is recommended for real-time applications. Suitability of the proposed method is validated by real-time implementation during the testing of the Autonomous Underwater Vehicle (AUV-150) developed indigenously by CSIR-CMERI. [ABSTRACT FROM AUTHOR]

Camera tampering may indicate that a criminal act is occurring. Common examples of camera tampering are turning the camera lens to point to a different direction (i.e., camera motion) and covering the lens by opaque objects or with paint (i.e., camera occlusion). Moreover, various abnormalities such as screen shaking, fogging, defocus, color cast, and screen flickering can strongly deteriorate the performance of a video surveillance system. This study proposes an automated method for rapidly detecting camera tampering and various abnormalities for a video surveillance system. The proposed method is based on the analyses of brightness, edge details, histogram distribution, and high-frequency information, making it computationally efficient. The proposed system runs at a frame rate of 20–30 frames/s, meeting the requirement of real-time operation. Experimental results show the superiority of the proposed method with an average of 4.4% of missed events compared to existing works. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hierarchical color correction algorithm for enhancing the color of digital images obtained from low-quality digital image capture devices such as cell phone cameras. The proposed method is based on a multilayer hierarchical stochastic framework whose parameters are learned in an offline training procedure using the well-known expectation maximization (EM) algorithm. This hierarchical framework functions by first making soft assignments of images into defect classes and then processing the images in each defect class with an optimized algorithm. The hierarchical color correction is performed in three stages. In the first stage, global color attributes of the low-quality input image are used in a Gaussian mixture model (GMM) frame-work to perform a soft classification of the image into M predefined global image classes. In the second stage, the input image is processed with a nonlinear color correction algorithm that is designed for each of the M global classes. This color correction algorithm, which we refer to as resolution synthesis color correction (RSCC), applies a spatially varying color correction determined by the local color attributes of the input image. In the third stage, the outputs of the RSCC predictors are combined using the global classification weights to yield the color corrected output image. We compare the performance of the proposed method to other commercial color correction algorithms on cell phone camera images obtained from different sources. Both subjective and objective measures of quality indicate that the new color correction algorithm improves quality over the existing methods. [ABSTRACT FROM AUTHOR]

ED light, a green energy-saving light source, can cause color cast. For this reason, LED light is seldom favored by designers. The purpose of the paper is to provide shoppers who are observing product colors in an LED-lighted setting with an innovative color identification model. Based on designers' product color comparison, the paper employs high-reliability mechanic visual perception in combination with grey relational grade. Grey relational grade is applied to eliminate electrical fault pertaining to mechanic visual perception, whereby appropriate LED parameters and color cast inclination can be obtained. The paper first mimics retail store display windows. The color temperature and illuminance of LED light sources are adjustable. Two degrees of illuminance, including high illuminance (1500 lux) and low illuminance (500 lux), and two light source color temperatures, including yellow light (2700 K) and white light (4000 K), were assigned for study. Four colors, including red, yellow, blue and green of the natural color system, were selected as product colors. The mechanic visual perception sensor was used to identify the object (product) color, which is then converted into an RGB color model to serve as research data of color cast measurement, and the grey relational grade was applied to obtain the most appropriate LED light parameters and the color cast of the four colors. The data analysis reveals that green shows the least color cast when it is lighted by a yellow LED light source with low illuminance, yellow and blue have the least color cast when it is lighted by a white LED light source with high illuminance and red displays the least color cast when it is lighted by a white LED light source with low-illuminance. The analysis also indicates each color's cast inclination in blackness, chromaticness and hue. As a result, LED light that is more acceptable to designers is suggested for display windows, thus reducing problems with product color cast. [ABSTRACT FROM AUTHOR]