Underwater Image Enhancement Method Research Articles

Enhancement of underwater optical images based on background light estimation and improved adaptive transmission fusion

Underwater optical imaging technology plays a vital role in humans' underwater activities. However, the serious quality degradation of underwater optical images hinders further development of such technology. This phenomenon is mainly caused by the absorption and scattering of light in the underwater medium. The blurred image formation model is widely used in the field of optical images and depends on two optical parameters: background light (BL) and the transmission map (TM). Therefore, we propose an underwater optical image enhancement method in the context of underwater optical image restoration and color correction. First, BL estimation based on the gray close operation, which can avoid the influence of white objects while accurately calculating BL, is proposed. Then, an improved adaptive transmission fusion (IATF) method is proposed, and the adjusted reversed saturation map (ARSM) method is applied to compensate for and refine the estimated TMs to obtain the final TMs. This paper also proposes a new underwater light attenuation prior (NULAP) method. Finally, to enhance color saturation and edge details, a statistical colorless slant correction fusion smoothing filter method is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for dehazing, color and detail enhancement, and (uneven) light intensity.

Depth-aware total variation regularization for underwater image dehazing

Underwater images often show severe quality degradation due to the light absorption and scattering effects in water medium. This paper introduces a scene depth regularized underwater image dehazing method to obtain high-quality underwater images. Unlike previous underwater image dehazing methods that usually calculate a transmission map or a scene depth map using priors, we construct an exponential relationship between transmission map and normalized scene depth map. An initial scene depth is first estimated by the difference between color channels. Then it is refined by total variation regularization to keep structures while smoothing excessive details. An alternating direction algorithm is given to solve the optimization problem. Extensive experiments demonstrate that the proposed method can effectively improve the visual quality of degraded underwater images, and yields high-quality results comparative to the state-of-the-art underwater image enhancement methods quantitatively and qualitatively.

Underwater image enhancement method with non-uniform illumination based on Retinex and ADMM

In order to solve the image blurring and distortion problem caused by underwater non-uniform and low illumination, this paper proposes an underwater image enhancement algorithm based on the Retinex theory and the Alternating Direction Method of Multipliers (ADMM). Firstly, the L component of the original image in the Lab space is extracted as the initial illumination map, and an Augmented Lagrange Multiplier (ALM) framework is constructed based on the ADMM to optimize the initial illumination map in order to obtain an accurate illumination image. In addition, the illumination map is further corrected in the luminance region with the Gamma Correction. Secondly, combined with the color constancy characteristics in the Retinex theory, the reflected image of the object is obtained. Finally, the bilateral filter is picked to suppress the underwater noise and obtain a more detailed enhanced image. The experimental results show that the underwater image enhancement algorithm can effectively solve the non-uniform illumination problem caused by natural light or artificial light source and improve the underwater image quality, thus having a better performance than other algorithms.

Underwater image enhancement by combining color constancy and dehazing based on depth estimation

The physical properties which are present in the underwater environment affects the images captured by the visual sensors. As a consequence of these properties, the captured image includes non-uniform illumination. This non-uniform illumination cause color distortion, low contrast, white regions, and color casts. An underwater image enhancement method is proposed by combining a color constancy framework and dehazing. In the color constancy framework, both the white patch retinex and gamma correction are used to illuminate a non-hardware based balanced artificial illumination in the reference image. A chromatic adaptation technique (CAT) is adapted to correct the color cast caused by the non-uniform illumination. The color transferred image is then transformed into HSI with gamma correction in the Intensity (I)-component. This gamma correction enhances the intensity of the color transferred image. The gamma-corrected HSI image is converted to an RGB image. The dehazing is based on the estimation of artificial background light and transmission map depth. The depth is estimated from the difference of channel intensity prior (DCIP), which is the difference between the maximum and minimum intensity priors. Further, normalization of the DCIP with the histogram stretching enhances the contrast of the estimated depth. A saturation correction factor is proposed for color correction. This correction factor, estimates the artificial background light, and solves the non-uniform illumination limitations in the turbid image. A guided and rolling guidance filter is adapted to refine the estimated transmission map depth. Finally, the recovered image is transformed into HSI image with gamma correction on the I-component. The gamma-corrected HSI image is transformed to an RGB image. The recovered RGB image results with enhanced contrast and brightness. The proposed method enhances the contrast, preserves the visual information such as texture smoothing, edge-preserving, no halo effect, and decreases artifacts. We experimented the proposed method with that of the existing methods and observed that the proposed method resulted in a substantially improved image quality for the human visual perception.

A Review of IFM-Free and IFM-Based Methods For Underwater Imaging

Underwater images play a key role in ocean exploration, however typically suffer from severe quality degradation because of light absorption and scattering in water medium. Researchers attempt to capture high-quality underwater pictures for a range of underwater applications. The particles suspended underwater generate unwanted noise and worsens the visibility of dimming pictures. The specialised hardware platforms and cameras may be pricey and power-consuming. Several algorithms are developed for underwater image quality improvement by image enhancement or restoration. To review current development of quality improvement methods for underwater images comprehensive comparisons on IFM-Based underwater image restoration methods and IFM-Free underwater image enhancement methods from both subjective and objective perspectives are used in this paper.. This paper introduces the essential principles of underwater imaging models with image enhancement and image restoration

A Review on the Underwater Image Restoration and Enhancement Methods

Light scattering, as well as light absorption in the water, often cause underwater images to be hazy, poorly contrasted, and dominated by either green or blue colour cast. In this paper, we review some of the state-of-the-art approaches in which specifically designed to enhance the quality of the acquired images. These approaches are able to eliminate the color cast and haziness on the images as well as to improve the image colourfulness and contrast. The characteristics of each of the developed approaches are highlighted, and their performances are evaluated both subjectively and objectively by the quality assessment methods.

Underwater image enhancement method based on adaptive attenuation-curve prior.

The attenuation (sum of absorption and scattering), which is caused by the dense and non-uniform medium, generally leads to problems of color degradation and detail loss in underwater imaging. In this study, we describe an underwater image enhancement method based on adaptive attenuation-curve prior. This method uses color channel transfer (CCT) to preprocess the underwater images, light smoothing, and wavelength-dependent attenuation to estimate water light and obtain the attenuation ratio between color channels, and estimates and refines the initial relative transmission of the channel. Additionally, the method calculates the attenuation factor and saturation constraints of the three color channels and generates an adjusted reverse saturation map (ARSM) to address uneven light intensity, after which the image is restored through water light and transmission estimation. Furthermore, we applied white balance fusion globally guided image filtering (G-GIF) technology to achieve color enhancement and edge detail preservation in the underwater images. Comparison experiments showed that the proposed method obtained better color and de-hazing effects, as well as clearer edge details, relative to current methods.

Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Aimed at the two problems of color deviation and poor visibility of the underwater image, this paper proposes an underwater image enhancement method based on the multi-scale fusion and global stretching of dual-model (MFGS), which does not rely on the underwater optical imaging model. The proposed method consists of three stages: Compared with other color correction algorithms, white-balancing can effectively eliminate the undesirable color deviation caused by medium attenuation, so it is selected to correct the color deviation in the first stage. Then, aimed at the problem of the poor performance of the saliency weight map in the traditional fusion processing, this paper proposed an updated strategy of saliency weight coefficient combining contrast and spatial cues to achieve high-quality fusion. Finally, by analyzing the characteristics of the results of the above steps, it is found that the brightness and clarity need to be further improved. The global stretching of the full channel in the red, green, blue (RGB) model is applied to enhance the color contrast, and the selective stretching of the L channel in the Commission International Eclairage-Lab (CIE-Lab) model is implemented to achieve a better de-hazing effect. Quantitative and qualitative assessments on the underwater image enhancement benchmark dataset (UIEBD) show that the enhanced images of the proposed approach achieve significant and sufficient improvements in color and visibility.

Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

Underwater image enhancement method based on the generative adversarial network

Aiming at the problems of color distortion, nonuniform illumination, and low contrast caused by degradation of underwater images, an underwater image enhancement method (MSFF-GAN) based on generative adversarial network was proposed. A multiscale featured fusion generator is designed, which improves the ability to use different scale features of the model and ensures that the generated image retains more detailed information. The residual dense module is constructed to solve the problem of generator characteristics extracted slower. In the discriminator, to achieve the extraction of local image features, the output matrix is discriminating so that the generated image is closer to the real image. Compared with the existing underwater image enhancement methods qualitatively and quantitatively, the proposed method has better enhancement effect on EUVP and RUIE datasets. The proposed method is superior to the contrast method of three evaluation indexes: PSNR, SSIM, and UIQM.

A novel intelligent underwater image enhancement method via color correction and contrast stretching✰

With the rapid development of artificial intelligence, image processing technology has been more and more widely used. Image enhancement is an important part of image processing, and has become a research hotspot of theory and application of image processing technology. This article proposes a new method for underwater image enhancement to solve the problems of color distortion, low contrast and blurring in underwater images. The compensation factor is used to compensate the badly damaged color channels, and the compensation factor is constructed by the mean differences between the damaged color channels and the well-preserved color channel. Then, multi-scale convolution MSRCR technology is used to denoising and correct color distortion, in conclusion, CLAHS and global contrast stretching are used to improve the local and global contrast of the images. Qualitative and quantitative evaluations prove that the proposed method can solve the color cast effect and improve the contrast of underwater images. The images processed by our method have natural color, high contrast and high clarity. Similarly, our method can also achieve good results in underwater low light and underwater images captured by different camera scenes.

Robust underwater image enhancement method based on natural light and reflectivity.

The poor visibility of underwater images is caused not only by scattering and absorption effects but is also related to light conditions. To improve robustness, a novel underwater image enhancement method based on natural light and reflectivity is proposed. Aiming at the scattering effects of reflectivity, a dehazing process based on the non-correlation of a foreground scene and background light is first conducted. Then, a more precise reflectivity can be estimated by substituting the captured image with the dehazed image. Moreover, classical methods often regard the dehazed image as the final result, but ignore the fact that attenuated natural light and nonuniform artificial light, which lead to insufficient brightness and halo effects, are included in the dehazed image, and are not robust to all scenes. This phenomenon enables us to remove the artificial light disturbance by introducing the dehazed image in the Lambertian model, and compensate for the loss of natural light energy by exploiting the light attenuation ratio map. Thus, the least-attenuated natural light can be further derived. Experimental results demonstrate that our method is satisfactory in producing more pleasing results under various circumstances.

Two-Branch Deep Neural Network for Underwater Image Enhancement in HSV Color Space

Due to the influence of light absorption and scattering, underwater images usually suffer from quality deteriorations such as color cast and reduced contrast. The diverse quality degradations not only dissatisfy the user expectation but also lead to a significant performance drop in many underwater vision applications. This letter proposes a novel two-branch deep neural network for underwater image enhancement (UIE), which is capable of separately removing color cast and enhancing image contrast by fully leveraging useful properties of the HSV color space in disentangling chrominance and intensity. Specifically, the input underwater image is first converted into the HSV color space and disentangled into HS and V channels to serve as the input of the two branches, respectively. Then, the color cast removal branch enhances the H and S channels with a generative adversarial network architecture while the contrast enhancement branch enhances the V channel via a traditional convolutional neural network. The enhanced channels by the two branches are merged and converted back into RGB color space to obtain the final enhanced result. Experimental results demonstrate that, compared with state-of-the-art UIE methods, our method can produce much more visually pleasing enhanced results.

Enhancement of Nonuniformly Illuminated Underwater Images

Underwater image capturing is a challenging task due to attenuation of light in water. Scattering and absorption are the results of light attenuation which lead to faded colors and reduced contrast of images, respectively. To deal with these issues and to provide better visual quality image, various enhancement methods have been proposed. This paper proposes the Dual Domain-based Underwater Image Enhancement (DDUIE) method. DDUIE method provides contrast stretching in approximation band of discrete wavelet transformed image followed by intensity adjustment of different color channels in spatial domain. To further improve the color quality, the image is processed in HSV (Hue–Saturation–Value) color space. Result analysis indicates better results for DDUIE method over state-of-the-art methods. Subjective results of DDUIE method show minimization of the bluish-green effect and reduction of nonuniform illumination up to a certain extent. These lead to enhanced color and image details. Quantitative results show that the Underwater Image Quality Measure (UIQM) and Underwater Color Image Quality Evaluation (UCIQE) values between 1 and 2 and between 0 and 1 have been achieved, respectively, which significantly illustrate that images have been enhanced efficiently and also entropy values between 7 and 8 depict the effectiveness of the proposed method in terms of image details.

Multiscale Fusion Method for the Enhancement of Low-Light Underwater Images

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.

Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.

Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing

The attenuation (sum of absorption and scattering), which caused by the dense and non-uniform medium, generally leads to problems of color degradation and detail loss in underwater imaging. To address these problems, we propose a systematic underwater image enhancement method, which includes an attenuation map guided underwater image color correction approach and a detail preserved dehazing approach. The color correction approach fully considers the main causes of color degradation in underwater imaging, namely wavelength-dependent attenuation of different colors. According to the attenuation map of each color channel, a piece-wise linear transform is used to process the information of each color channel. Then, the detail preserved dehazing approach based on multi-scale decomposition is proposed to compensate for the lost details while eliminating the effects of haze. Especially, an adaptive Maximum Intensity Prior (MIP) measurement based on maximum attenuation identification is proposed to estimate transmission of the medium. Experiments on a variety types of degraded underwater images have proven that our proposed method can produce accurate results with vivid color and fine details, even better than other state-of-the-art underwater image dehazing methods.

Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazed image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light and the transmission map. 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 via the statistic of clear and high resolution underwater images, then a scene depth map based on the underwater light attenuation prior and an adjusted reversed saturation map 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 channel and G-B channels. Finally, to improve the color and contrast of the restored image with a 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, less computation time, more superior performance, and more valuable information retention.

Benchmarking Underwater Image Enhancement and Restoration, and Beyond

Image enhancement and restoration is among the most investigated topics in the field of underwater machine vision. The objective image quality assessment is a fundamental part of optimizing underwater enhancement and restoration technologies. However, most no-reference (NR) metrics are not specifically designed for underwater image quality assessment. Moreover, since the reference (undegraded) images are not available in underwater scenes, the classical full-reference (FR) metrics cannot be used to evaluate underwater image enhancement and restoration methods. In this paper, we first design an underwater image synthesis algorithm (UISA), in which depending on the real-world underwater image, we can produce a synthetic underwater image from an outdoor ground-truth image. Based on this strategy, we establish a new large-scale benchmark that contains ground-truth images and synthetic underwater images of the same scene, called synthetic underwater image dataset (SUID). Our SUID is constructed on the basis of the underwater image formation model (IFM) and characteristics of underwater optical propagation, possessing solid reliability and feasibility. The proposed SUID creates possibility for a FR evaluation of existing technologies for underwater image enhancement and restoration, which is illustrated by performing extensive experiments and quantitative analysis. The SUID is available online at: http://dx.doi.org/10.21227/agdr-y109.

An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering.

In the shallow-water environment, underwater images often present problems like color deviation and low contrast due to light absorption and scattering in the water body, but for deep-sea images, additional problems like uneven brightness and regional color shift can also exist, due to the use of chromatic and inhomogeneous artificial lighting devices. Since the latter situation is rarely studied in the field of underwater image enhancement, we propose a new model to include it in the analysis of underwater image degradation. Based on the theoretical study of the new model, a comprehensive method for enhancing underwater images under different illumination conditions is proposed in this paper. The proposed method is composed of two modules: color-tone correction and fusion-based descattering. In the first module, the regional or full-extent color deviation caused by different types of incident light is corrected via frequency-based color-tone estimation. And in the second module, the residual low contrast and pixel-wise color shift problems are handled by combining the descattering results under the assumption of different states of the image. The proposed method is experimented on laboratory and open-water images of different depths and illumination states. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms many other methods in enhancing the quality of different types of underwater images, and is especially effective in improving the color accuracy and information content in badly-illuminated regions of underwater images with non-uniform illumination, such as deep-sea images.