Underwater Image Contrast Research Articles

Underwater image contrast enhancement through an intensity-randomised approach incorporating a swarm intelligence technique with unsupervised dual-step fusion

ABSTRACT Analysing objects in an underwater medium is difficult due to light problems in such an environment. Low contrast and visibility in water medium result in restricted information extraction. Some previous methods inadequately reduce underwater colour cast and improve the image contrast minimally. This work proposes intensity-randomised underwater image contrast enhancement (IRUCE), an improved enhancement method that integrates an unsupervised dual-step fusion technique to reduce blue-green colour cast, improve contrast, and enhance image brightness, especially in turbid and deep underwater media. IRUCE enhances the inferior colour channels and moderates the dominant colour channel to reduce the colour cast before the intensity-randomised approach is implemented. The intensity-randomised approach is designed to effectively distribute image intensity across all intensity levels. Next, a swarm intelligence algorithm is fused to perform median equalisation on all colour channels. The median intensity values of inferior colour channels are shifted towards the dominant colour channel. The unsharp masking technique is employed in the last step to increase image sharpness. The effectiveness of this approach is validated through quantitative and qualitative evaluations, and the results are compared with those of other state-of-the-art methods. Outcomes indicate that the proposed method improves underwater image quality significantly and outperforms other contemporary techniques.

An underwater imaging method of enhancement via multi-scale weighted fusion

Blurring and color distortion are significant issues in underwater optical imaging, caused by light absorption and scattering impacts in the water medium. This hinders our ability to accurately perceive underwater imagery. Initially, we merge two images and enhance both the brightness and contrast of the secondary images. We also adjust their weights to ensure minimal effects on the image fusion process, particularly on edges, colors, and contrast. To avoid sharp weighting transitions leading to ghost images of low-frequency components, we then propose and use a multi-scale fusion method when reconstructing the images. This method effectively reduces scattering and blurring impacts of water, fixes color distortion, and improves underwater image contrast. The experimental results demonstrate that the image fusion method proposed in this paper effectively improves the fidelity of underwater images in terms of sharpness and color, outperforming the latest underwater imaging methods by comparison in PSNR, Gradient, Entropy, Chroma, AG, UCIQE and UIQM. Moreover, this method positively impacts our visual perception and enhances the quality of the underwater imagery presented.

Underwater Image Enhancement Based on Adaptive Color Correction and Improved Retinex Algorithm

In order to solve the problems about color distortion and low contrast of underwater images, we propose an underwater image enhancement algorithm that combines adaptive color correction with improved Retinex algorithm. Our algorithm is a single-image enhancement method that does not require specialized hardware and underwater scenes prior. Firstly, the adaptive color correction is carried out on the underwater distorted images to solve the color cast problem effectively. Then, on the one hand, we use the image decomposition to strengthen the detail part and obtain a detail enhanced image. On the other hand, we use the improved Retinex algorithm to strengthen the edge part and obtain an edge enhanced image. Finally, the detail enhanced image and the edge enhanced image are fused based on the non-subsampled shearlet transform (NSST) to obtain the final enhanced underwater image. The results show that our method outperforms several state-of-the-art methods about underwater image enhancement in terms of PCQI, UCIQE, UIQM and IE. By scale invariant feature transform (SIFT) algorithm, we calculate the number of feature matching points of the input image and the enhanced image, and our proposed method achieves the best experimental results. The source code of our proposed algorithm is available at: https://github.com/lin9393/ underwater-image-enhance.

Underwater Image Enhancement Based on Color Balance and Multi-Scale Fusion

Underwater light absorption and scattering lead to color deviation, low brightness, fuzzy details and low contrast of underwater images. In this contribution, an underwater image enhancement algorithm based on color balance and multi-scale fusion is proposed. Firstly, a color balance method is used to correct the image color. Then, based on an improved dark channel prior algorithm, the local contrast information of the color balanced image is used to derive two atmospheric lights and transmission maps, which are transformed into the images with enhanced contrast and brightness, respectively. Finally, the multi-scale fusion method is adopted to fuse the contrast enhanced image and the brightness improved image according to the weights. The proposed algorithm is compared with other underwater image enhancement algorithms in qualitative and quantitative evaluation. Experimental results show that the proposed algorithm can effectively eliminate color deviation, remove dark areas, and improve brightness and contrast of underwater images. The enhanced images generated by the proposed algorithm are superior to those generated by other algorithms in indicators of PSNR, SSIM, UIQM and UCIQE. Thus, the proposed algorithm is suitable for underwater image enhancement.

Reduction of Artifacts and Edge Preservation of Underwater Images Using Deep Convolution Neural Network

Underwater exploration has been one of the areas of active research over the last few decades. Image enhancement is difficult for computer vision-based underwater research because of the degradation of the underwater environment’s images. Several types of artifact reduction approaches are already available of value averaging filters to smooth the continuity that artifact reduction approaches appear across image boundaries. While some of these approaches can somewhat reduce these unwanted artifacts’ severity, other approaches have some limitations that can cause blurring to high-contrast edges in the image. Therefore we need novel methods to overcome the theses’ drawbacks. This research work introduced a Deep Convolution Neural Network (DCNN)-based deep learning method to overcome the drawbacks of blurring to low-contrast edges in the image. The proposed system’s performance is validated through simulation, and the simulation results are obtained using Python simulation software. The obtained simulation results demonstrate the superiority of the proposed underwater image enhancement method. The number of different coefficients is compared with the results of the algorithm of the Underwater Image Colorfulness Measure (UICM), Underwater Image Sharpness Measure (UISM), Underwater Image Contrast Measure (UIconM) and Underwater Image Quality Measure (UIQM) values. After that, it was found to be an effective comparison of visualization techniques.

Underwater image restoration via backscatter pixel prior and color compensation

The use of underwater cameras instead of divers to enter complex underwater areas for real-time monitoring of fish, shrimp, and algae is significant to the aquaculture industry. However, underwater images are severely degraded due to light absorption and scattering, limiting the development of underwater computer vision and robot vision perception. To solve blurriness and color degradation issues, this paper developed a restoration method based on backscatter pixel prior and color cast removal from the physical point of view of underwater image degradation. The proposed method used only a single underwater image as an input to estimate various parameters accurately, such as depth map, backscatter map, and illuminant map. Specifically, a backscatter estimation algorithm based on a depth map was proposed to improve the contrast of underwater images. Then, an algorithm was designed to remove color deviation based on the illuminant map. In particular, a color compensation strategy was created that could completely eliminate red artifacts in underwater images that were generated by the strong attenuation of the red channel. We designed comparative experiments from multiple angles on different real underwater image datasets. Experiments showed that the proposed method improved the contrast and removed the color deviation of light absorption compared to several reported methods. Even on underwater images with severe attenuation, the proposed method showed a significant positive effectiveness and stability on color cast removal.

Retinex-inspired color correction and detail preserved fusion for underwater image enhancement

Many practical underwater applications need to address color cast, blurring, and low contrast of underwater images for display and further analysis. To cope with the aforementioned issues, a novel underwater image enhancement method based on Retinex-inspired color correction and detail preserved fusion technology is proposed. First, we employ a Retinex-inspired color correction strategy to remove the color cast induced by underwater light scattering. We then build on blending three images directly derived from a local contrast-enhanced version, a detail version, and a global contrast-enhanced version of the corrected underwater image. The local and global contrast-enhanced versions promote the transfer of color and contrast to the output image. Meanwhile, the detail version improves the edges and details of the output image. Qualitative and quantitative comparisons demonstrated that the proposed method achieves superior enhancement performance for underwater images of different scenes.

A New Defogging Algorithm for Underwater Images

Abstract Images taken underwater will appear greenish or blueish due to the scattering and attenuation of light, resulting in color distortion of underwater images. This paper proposes a new underwater image defogging algorithm, which can effectively remove image noise, improve the color saturation and contrast of underwater images, and restore clear underwater images without fog. The algorithm introduces guided filtering, which increases speed and work efficiency. The linear enhancement of the saturation component can effectively enhance the color saturation of the underwater image. Compensation for local brightness can better show image details. Through experimental comparison with other algorithms, our algorithm has achieved good advantages in terms of defogging, image structural information integrity, and resistance to color attenuation.

Research on underwater image enhancement algorithm based on improved DCP

All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.

Edge Aware Turbidity Restoration of Single Shallow Coastal Water Image

The blur and low contrast of underwater images are indicative of the high noise, intense scattering and ultimately the low quality of such images. Image Enhancement and Restoration are imperative pre processing steps for single images from shallow coastal areas. Pre processing of underwater images is hence a prerequisite to processes like classification, object detection and computer vision. The paper presents an effective edge aware restoration models that goals turbid water images. The dark channel based restoration method with a rolling guidance filter gives a more edge aware restored and denoised version of the heavily blurred, low contrast shallow coastal image. This is evident from the subjective and objective projections. Moreover the edge preserving nature projects 13.05% and 5.53% more than UHP (which is in close) in terms of number of edges and UCIQE scores in comparisons with promising algorithms over the decade.

Underwater images contrast enhancement and its challenges: a survey

Exploration of the deep sea and ocean in the marine industry has continued to gain interest in recent years. To get the detailed imaging of deep sea layers, marine vessels and robots are fitted with advanced imaging technologies. There are certain factors like water properties and impurities that affect the quality of the photographs captured by the underwater imaging devices. As sea water absorbs colors, so processing of sea imaging data becomes more challenging. Water light attenuation is a phenomenon that is caused by the absorbance and scattering factors. Certain studies showed that the existence of certain intrinsic shortcomings are attributed to the appearance of objects and ambient noise in underwater images. As a result, it is difficult in a real-time system to distinguish objects from their surroundings in these images. We measures the algorithms performance with respect to various aspects, effect of the hardware and software parts for underwater images and critical review of different underwater image enhancement algorithms. First, we describe some well-known techniques of spatial and frequency domains. Then, we list the existing quantitative measurements which are required to measure the quality of the enhanced image. Finally, the performance of various up-to-date existing methods is compared based on the outcomes of standard quantitative measurements, and factors such as requirements/suitability, and technical aspects, are included. Furthermore, a variety of image databases used for image contrast enhancement is discussed in detail. This study expands the scope for other researchers to understand the important characteristics of different underwater image contrast enhancement methods, and also provides future research directions.

Color correction and adaptive contrast enhancement for underwater image enhancement

To address the color cast and low contrast of underwater images, we propose a color correction and adaptive contrast enhancement algorithm for underwater image enhancement. In our work, we first design the dedicated fractions to compensate the lower color channels, which are calculated by considering the ratio of the differences between the upper and lower color channels to the lower color channels. Subsequently, an adaptive contrast enhancement algorithm is applied to each color channel is proposed to produce the background-stretched and foreground-stretched images, the combination of these two images significantly improves the contrast of the output image. Finally, a unsharp masking technique is applied to sharpen the overall image. Experiments on the standard underwater image enhancement benchmark dataset have proven that our algorithm can output high-quality images, while the performer of our algorithm is at least 5% better than the second-best in the average of all metric scores.

A multifeature fusion method for the color distortion and low contrast of underwater images

Light is scattered and absorbed when it is transmitted through seawater. Underwater images captured in water suffer from color attenuation, low contrast, and blurred details. To address these effects, we propose a multifeature fusion method (MFFM) to improve the color distortion and low contrast of underwater images. White balance technology is used to address the color cast of underwater images and obtain a color correction image. On this basis, the color correction image is enhanced by a guided filter to obtain a contrast-enhanced image. Four feature weights of the input images are calculated separately, and two normalized weight maps are constructed by normalization. To avoid a halo, the two normalized weight maps are multiscale fused. To improve the edge detail, the fused image is enhanced by a color adjustment technique to obtain the final enhanced image. Real underwater images are used for simulation and application testing. Compared with the current state-of-the-art methods, our method can balance the color and contrast and improve the visibility of underwater images. Extensive qualitative and quantitative experiments verify the superiority of the MFFM.

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.

Underwater Image Restoration and Enhancement Based on a Fusion Algorithm With Color Balance, Contrast Optimization, and Histogram Stretching

A fusion algorithm is proposed for the restoration and enhancement of underwater images. Color balance, contrast optimization and histogram stretching are carried out. To alleviate the effect of color shift in an underwater image, the scalar values of R, G, B channels are renewed so that the distributions of the three channels in histogram are similar. Instead of refining the transmittance in dark channel prior based restoration, an optimized contrast algorithm is employed by which the optimal transmittance is determined. To further improve the brightness and contrast of underwater images, a histogram stretching algorithm based on the red channel is given. To verify the effectiveness of the proposed fusion algorithm, experimental underwater images are treated. Results show that the quality of underwater images is improved significantly, both in term of subjective visual effect and objective evaluation. The proposed underwater image processing strategy is also compared with some popular techniques. Comparison results indicate the advantage of the proposed strategy over others.

Underwater image recovery method based on hyperspectral polarization imaging

The quality of underwater imaging can be severely degraded by particles in the turbid water due to absorption and backscatter effects. This study proposes an underwater image recovery method based on hyperspectral polarization imaging. The proposed method is based on a physical degradation model, and combines polarization recovery and spectral fusion methods. Thus, the optimal spectral fusion is realized according to the spectral selectivity of the polarization recovery method, leading to underwater imaging with improved clarity as well as effective colour information restoration. The experimental results show that, compared with other methods, the comprehensive restorative abilities of the method proposed in this study are better with respect to subjective vision and various image quality evaluation indexes. The proposed method is effective for enhancing the visibility and contrast of underwater images for objects comprising different materials and at different imaging distances. Consequently, the proposed method has potential application value in many underwater complex environments.

Underwater image restoration based on secondary guided transmission map

The color distortion and low contrast of underwater images are caused by the absorption of light by water and the scattering of suspended particles. So, we propose an underwater image restoration method based on secondary guided transmission map, which can effectively restore the color, visibility and natural appearance of underwater image. We use improved guided filter to refine the transmission map. Firstly, the rough transmission map is decomposed into the basic image and the detail image by the guided filter. And then refined transmission map is reconstructed after processing the images respectively. Finally, auto level processing is conducted on the restored image to improve the contrast of the image. In order to evaluate the effectiveness of our method, qualitative and quantitative comparisons and application test are carried out. Experimental results demonstrate that compared with several state-of-the-art methods, the proposed method can get genuine color, natural appearance, and the improvement of visibility and contrast.

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.

Optimised enhancement scheme for low contrast underwater images

Images acquired in underwater environments are usually affected by light absorption and scattering. These are the two phenomena that reduce the clarity of images that are captured in these environments. These factors cause low contrast and anamorphic colour diffusion. To tackle these issues, we propose an optimized low contrast enhancement scheme. The main thrust of this paper borders on enhancement of underwater image contrast by preserving the brightness level. The approach is termed Fuzzy-Histogram Equalisation Optimised for Brightness Preservation (FHEOBP) technique, where a combination of fuzzy and classical histogram equalisation techniques is employed towards the enhancement of the contrast of images from underwater scene. The scheme is optimized using teaching-learning-based optimisation technique that is built into the algorithm. The proposed FHEOBP filter shows improved performance over Local Histogram Equalisation (LHE) and Global Histogram Equalisation (GHE) as it has a higher luminance distortion index value than those of LHE and GHE. This translates into a better image details preservation. In fact, the computed luminance distortion indices for optimised FHEOBP are 16.4%, 28.3% and 20.1%, respectively higher than those of the corresponding GHE, in the same test images utilised for performance evaluation. Between the optimised and non-optimised FHEOBP, luminance distortion figures of optimised FHEOBP are 8%, 6.8% and 9.8% higher than those of the equivalent non-optimised FHEOBP in the test image data set.

Image descattering and absorption compensation in underwater polarimetric imaging

Underwater image contrast degrades and color distorts due to wavelength attenuation caused by scattering and absorption, and the attenuation rate of different wavelengths is significant different in variety of underwater environment, which leads to the satisfying underwater image restoration very difficult. We propose a novel underwater polarimetric image restoration method that consists of descattering and absorption compensation, which particularly takes into account wavelength- and scenedepth- dependent absorption existed in underwater signal light that is ignored by current polarimetric descattering methods. In the descattering step, an automatic attenuation difference map based background region selection strategy is presented, which can estimate background light without considering the no-object region exists or not. In the absorption compensation step, a novel absorption compensation constraint strategy is introduced into the color restoration algorithm for the detected signal light. The strategy relies on well-preserved color component and an absorption prior constraint. Extensive experiments on real-world underwater images demonstrate that the proposed method is more effective than several previous underwater image restoration or enhancement works.