An Improved Approach for Underwater Image Enhancement Through Color Correction, Contrast Synthesis and Dehazing

Due to the absorption and scattering effect on light when traveling in water, underwater images exhibit serious weakening such as color deviation, low contrast, and blurry details. Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation. To address these problems, a new approach for single underwater image enhancement based on fusion technology was proposed in this article. First, the original image is preprocessed by the white balance algorithm and dark channel prior dehazing technologies, respectively; then two input images were obtained by color correction and contrast enhancement; and finally, the enhanced image was obtained by utilizing the multiscale fusion strategy which is based on the weighted maps constructed by combining the features of global contrast, local contrast, saliency, and exposedness. Qualitative results revealed that the proposed approach significantly removed haze, corrected color deviation, and preserved image naturalness. For quantitative results, the test with 400 underwater images showed that the proposed approach produced a lower average value of mean square error and a higher average value of peak signal-to-noise ratio than the compared method. Moreover, the enhanced results obtain the highest average value in terms of underwater image quality measures among the comparable methods, illustrating that our approach achieves superior performance on different levels of distorted and hazy images.

Efficient underwater visual environment perception is the key to realizing the autonomous operation of underwater robots. Because of the complex and diverse underwater environment, the underwater images not only have different degrees of color cast but also produce a lot of noise. Due to the existence of noise in the underwater image and the blocking effect in the process of enhancing the image, the enhanced underwater image is still rough. Therefore, an underwater color-cast image enhancement method based on noise suppression and block effect elimination is proposed in this paper. Firstly, an automatic white balance algorithm for brightness and color balance is designed to correct the color deviation of underwater images and effectively restore the brightness and color of underwater images. Secondly, aiming at the problem of a large amount of noise in underwater images, a noise suppression algorithm for heat conduction matrix in the wavelet domain is proposed, which suppresses image noise and improves the contrast and edge detail information of underwater images. Thirdly, for the block effect existing in the process of enhancing the underwater color-cast image, a block effect elimination algorithm based on compressed domain boundary average is proposed, which eliminates the block effect in the enhancement process and balances the bright area and dark area in the image. Lastly, multi-scale image fusion is performed on the images after color correction, noise suppression, and block effect elimination, and finally, the underwater enhanced image with rich features is obtained. The results show that the proposed method is superior to other algorithms in color correction, contrast, and visibility. It also shows that the proposed method corrects the underwater color-cast image to a certain extent and effectively suppresses the noise and block effect of the underwater image, which provides theoretical support for underwater visual environment perception technology.

Single underwater image enhancement using integrated variational model

Color correction and restoration based on multi-scale recursive network for underwater optical image

Underwater image enhancement method based on golden jackal optimization

As remotely operated underwater vehicles (ROV) and static underwater video and image collection platforms become more prevalent, there is a significant need for effective ways to increase the quality of underwater images at faster than real-time speeds. To this end, we present a novel state-of-the-art end-to-end deep learning architecture for underwater image enhancement focused on solving key image degradations related to blur, haze, and color casts and inference efficiency. Our proposed architecture builds from a minimal encoder-decoder structure to address these main underwater image degradations while maintaining efficiency. We use the discrete wavelet transform skip connections and channel attention modules to address haze and color corrections while preserving model efficiency. Our minimal architecture operates at 40 frames per second while scoring a structural similarity index (SSIM) of 0.8703 on the underwater image enhancement benchmark (UIEDB) dataset. These results show our method to be twice as fast as the previous state-of-the-art. We also present a variation of our proposed method with a second parallel deblurring branch for even more significant image improvement, which achieves an improved SSIM of 0.8802 while operating more efficiently than almost all comparable methods. The source code is available at https://github.com/alejorico98/underwater_ddc

As human beings continue their way to find more and more resources beneath water and ocean, it becomes more urgent to have a very clear and detailed underwater image for us to explore the world unseen in the water. However, with the light propagating into the water, it is absorbed and scattered along the way which makes the underwater image unclear, hazy, detail-lost and color-shifted. Obviously, it is not the image we wish for. In the paper, the proposed method aims to enhance underwater image adaptively via color channel compensation based on optical image model. In the beginning the underwater image is restored only in green channel aiming to reduce the haze effect, then an adaptive color channel compensation is applied to correct the shifted color, lastly a multi-scale fusion is executed to show more image details after a white balance operation. Going for massive experiments, the proposed adaptive method fits in versatile scenes adaptively of greenish, bluish and turbid water body producing eye-friendly haze cover removed, color shift corrected, detail enhanced clear result image. Particularly the proposed method highly reduces the reddish effect after execution compared to many other state of art underwater image enhancement algorithm, while quantitatively the proposed method gives a better score too by underwater image quality measure (UIQM) and underwater color image quality evaluation (UCIQE).

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

Underwater images hold immense value for various fields, including marine biology research, underwater infrastructure inspection, and exploration activities. However, capturing high-quality images underwater proves challenging due to light absorption and scattering leading to color distortion, blue green hues. Additionally, these phenomena decrease contrast and visibility, hindering the ability to extract valuable information. Existing image enhancement methods often struggle to achieve accurate color correction while preserving crucial image details. This article proposes a novel deep learning-based approach for underwater image enhancement that leverages the power of autoencoders. Specifically, a convolutional autoencoder is trained to learn a mapping from the distorted colors present in underwater images to their true, color-corrected counterparts. The proposed model is trained and tested using the Enhancing Underwater Visual Perception (EUVP) and Underwater Image Enhancement Benchmark (UIEB) datasets. The performance of the model is evaluated and compared with various traditional and deep learning based image enhancement techniques using the quality measures structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and mean squared error (MSE). This research aims to address the critical limitations of current techniques by offering a superior method for underwater image enhancement by improving color fidelity and better information extraction capabilities for various applications. Our proposed color correction model based on encoder decoder network achieves higher SSIM and PSNR values.

Objective: Due to the problems of light propagation underwater, such as scattering and absorption, which leads to low contrast, color distortion and blurring of details in the images obtained underwater, this phenomenon is more serious in the deep sea, and most undersea map images collected by manned submersibles during deep-sea exploration exhibit these issues. To address these problems, an underwater image fusion enhancement algorithm based on color correction and image sharpening is proposed for image enhancement and restoration. Methods: The automatic color enhancement algorithm is used to correct and enhance the color of the underwater image. Subsequently, the RGB three channels are corrected by gamma filtering. The RGB space is then converted to the Lab space, and the L channel is processed by the Contrast Limited Adaptive Histogram Equalization algorithm to enhance the luminance. At the same time, the color-corrected image is image-sharpened by using unsharpened mask algorithms. Finally, image fusion is performed by using the algorithm based on the fusion of guided filters. Results: The experimental results on several underwater images under different scenarios show that the enhancement effect achieved by this algorithm is better than the comparison algorithm. In the subjective aspect, the color and details of the images are better balanced and enhanced. In the objective aspect, quantitative evaluations are carried out in three aspects: Information entropy (IE), underwater image quality measure (UIQM), and underwater color image quality evaluation (UCIQE), in which the IE is quantitatively evaluated. Regarding the UCIQE, the proposed algorithm has a better effect than other algorithms in terms of IE, UIQM, and UCIQE. Specifically, compared to the original image, the proposed algorithm shows improvements of 33.4%, 1.45 times, and 54.4% or more in mean value. Conclusion: The results show that the algorithm has good results in image processing for underwater environments, with enhancement and restoration of images acquired through manned submersibles.

Underwater images often suffer from color and contrast degradation, because the light is absorbed and scattered while traveling in water. Although the physical process of the underwater images seems similar to the outdoor haze images, conventional dehazing methods fail to generate accurate results since colors associated to different wavelengths have different attenuation rates in underwater conditions. To overcome this, we propose a novel underwater image restoration method based on color correction and image dehazing. First, we estimate the global background light using a hierarchical search based on quad-tree subdivision combined with the ocean optical properties. According to the properties of underwater optical imaging, we then introduce an underwater color correction method using depth compensation, in which a multi-channel guided image filter is proposed to refine the depth image. Finally, we adopt the non-local image dehazing algorithm to restore the underwater images. Experimental results demonstrate that the restored images can achieve better visual quality of underwater images when compared with several state-of-the-art methods.

In turbid water, the attenuation and scattering of light caused by scatterers make underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images. To address such problems, a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed in this article. First, an SR algorithm framework for underwater image enhancement is constructed, including the dimensionality reduction and normalization of input images, the solution and parameter optimization of the SR system, the dimensionality upgrading of output images, etc. This framework can apply the SR's ability to enhance weak signals to the enhancement of turbid underwater images. Second, to measure the performance of the system, a synthetic turbid underwater image data set (UWCHIC) is constructed using the underwater imaging model and an image set with simulated scatterers. Based on this data set, the relationship between various image quality evaluation metrics and system parameters is analyzed, and then the suitable no-reference (NR) metrics for system performance evaluation are selected and an adaptive parameter tuning strategy of the SR system is proposed to guide the image enhancement. Lastly, the proposed method is evaluated on the UWCHIC, a dataset to evaluate underwater image restoration methods (TURBID), marine underwater environment database (MUED), and underwater image enhancement benchmark (UIEB) data sets and the turbid underwater images captured from natural waters. Different experimental evaluations demonstrated that the proposed method not only effectively enhances the visual quality of turbid underwater images but also improves the performance of downstream vision tasks.

Fusion of multiscale gradient domain enhancement and gamma correction for underwater image/video enhancement and restoration

Underwater images often suffer from many typical problems: in a complex optical environment, the quality of underwater images drops sharply, and features such as color and brightness are often attenuated seriously, which makes it difficult to improve the quality of underwater images. Polarization imaging can effectively suppress underwater scattering. In the underwater imaging environment, according to the polarization characteristics of the signal, backscatter and forwardscatter light, the impact of different components on the image is solved. Based on the underwater physical imaging model and the principle of polarization imaging, the principle of underwater polarization imaging is described in detail, and several classic underwater polarization imaging methods are emphasized. The current underwater imaging technology based on polarization characteristics is summarized, and according to their actual effect, these methods are evaluated and analyzed. What's more, based on the advantages and disadvantages of the existing underwater polarization imaging technology and their actual results, the future development of the underwater polarization imaging technology is summarized.