Underwater images play a key role in ocean exploration but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. First, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of the state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analyses (the used code is freely available at https://github.com/wangyanckxx/Single-Underwater-Image-Enhancement-and-Color-Restoration). Starting from this paper, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Integer-based accurate conversion between RGB and HSV color spaces

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

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at this https URL). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Images captured underwater usually suffer from color distortion, detail blurring, low contrast, and a bluish or greenish tone due to light scattering and absorption in the underwater medium, which in turn the visibility is adversely affected by these factors seriously. Over the last decades, various image restoration and enhancement methods have been developed by many researchers to improve the quality (visibility and highlight richer details) of underwater images. This paper introduces the overview of state-of-the-art underwater image restoration and enhancement techniques and classifies the approaches in two categories: image restoration (physical-based model) and image enhancement (nonphysical-based model). Furthermore, the classification of these two methods is elaborated. Then, the typical underwater image restoration and enhancement methods are discussed in detail, as well as a comprehensive study and fair evaluation of the methods is carried out from both qualitative and quantitative perspectives. Finally, the research process of underwater image restoration and enhancement is summarized and the suggestions for future research are prospected.

A Survey of Restoration and Enhancement for Underwater Images

For undersea activities, underwater vehicles usually carry optical imaging systems for recording The captured images and videos frequently suffer from two displeasing problems: First, color distortion; and second, poor visibility This is mainly because that the light is exponentially attenuated while penetrating through water and the strength of attenuation is color dependent This paper develops an effective single underwater image restoration framework based on the depth estimation and the transmission compensation To address the consequences of scattering and absorption, our restoration scheme consists of five major phases: 1) background light estimation; 2) submerged dark channel prior; 3) transmission refinement and radiance recovery; 4) point spread function deconvolution; and 5) transmission and color compensation A wide variety of underwater images with various scenarios were exploited to assess the restoration performance of the proposed algorithm Various evaluation metrics were employed to analyze the experimental results It was suggested that this new restoration algorithm outperformed many state-of-the-art methods both qualitatively and quantitatively In addition, potential applications regarding autopilot and three-dimensional visualization were demonstrated We believe that our underwater image restoration technique is promising in many undersea activities that require high-quality images

Single Underwater Image Restoration Based on Depth Estimation and Transmission Compensation

In the field of undersea research, underwater vehicles usually carry camera systems for recording. The captured image or video often has two undesired characteristics: color distortion and low visibility. This is because that the light is exponentially attenuated while penetrating through water. Furthermore, the quality of attenuation is associated with the wavelength of spectrum. This paper simplifies the Jaffe-McGlamery optical model and proposes an effective algorithm to recover underwater images. In our approach, a red-dark channel prior was defined and derived to estimate the background light and the transmission. The visibility of scene was compensated by the object-camera distance to recover the colors of the background and objects. Subsequently, by analyzing the physical property of the point spread function, we developed a simple but efficient low-pass filter to deblur degraded underwater images. A wide variety of underwater images with different scenarios were used for the experiments. The experimental results indicated that the proposed algorithm effectively recovered underwater images while eliminating the influence of absorption and scattering. We believe that this new restoration algorithm is promising in many underwater image processing applications.

Underwater image restoration by red-dark channel prior and point spread function deconvolution

When light is transmitted in water from a subject to an observer, it is scattered and absorbed by the unstable environment such as suspended particles and turbid water Due to these phenomena, underwater images usually have poor quality including low contrast, blurring, darkness, and color diminishing In this paper, we propose a new underwater image restoration algorithm that consists of two major phases: visibility restoration and fidelity restoration In the first phase, underwater images are observed similar to haze images because they have the same problems of low contrast and color shifting This motivated us to use the haze removal technique, namely, dark channel prior, to dehaze underwater images Subsequently, in the second phase, we equalize the color mean in each RGB (red, green, blue) channel to balance the color Then transform the color space from RGB to HSV (hue, saturation, value) color space to adjust S channel to make the image color more natural Finally, we adjust V channel according to the brightness value of RGB to enhance the contrast Preliminary results indicated that the proposed method effectively improved visibility and fidelity of underwater images

Improving visibility and fidelity of underwater images using an adaptive restoration algorithm

Clear underwater vision is crucial to many research and realistic applications in ocean engineering, ocean biology, and ocean science. However, owing to particular propagation properties of light beneath water, underwater images usually have poor quality including low contrast, blurring, darkness, and color diminishing. In this article, a new underwater image restoration algorithm is proposed that consists of two major phases: haze removal and color correction. In the first phase, underwater images are observed similar to haze images because they have the same problems of low contrast and color shifting. This motivated us to use the dark channel prior haze removal technique to dehaze underwater images. Subsequently, in the second phase, we equalize the color mean in each RGB (red, green, blue) channel to balance the color. We use the CLAHE method to further increase the local contrast of the image. Then, the color space is transformed from RGB to HSV (hue, saturation, value) to adjust the S channel to make the image color more natural. Finally, we adjust V channel according to the brightness value of RGB to enhance the global contrast. Preliminary results indicated that the proposed method effectively improved visual interpretation of underwater images.

Restoration of underwater vision using a two-phase regularization mechanism

Underwater imaging is crucial to a wide variety of research and realistic applications in marine biology, water fauna identification and assessment, archaeology, mine detection, oceanic mapping, and autonomous underwater robotics. However, due to specific propagation properties of light in water such as absorption and scattering as well as unstable environment such as light changing and water turbidness, the images captured are highly disturbed with low contrast, blurring, darkness, and color diminishing. This paper proposes a new underwater image restoration algorithm that consists of three major phases: haze removal, color correction, and contrast enhancement. To estimate the transmission coefficient function, we first compute the dark channel map, which is the set of “dark” pixels having very low intensity values in at least one RGB color channel. To accommodate the blue color distortion phenomenon, the red channel value is excluded from the calculation if the peak histogram intensity is smaller than a specified threshold. To optimize the medium transmission map, we adopt the matting Laplacian matrix associated with the sparse linear system to generate a cost function, followed by the guided filtering method to accelerate the computation. Finally, the contrast limited adaptive histogram equalization method is used to enhance the contrast while maintaining the color fidelity. We have applied this new approach to a wide variety of underwater images. Experimental results indicated that this new method is of potential in facilitating the interpretation and perception of underwater images in the fields of ocean engineering, ocean biology, and ocean science.

Chia-Chi Sung

Papers

Single Underwater Image Restoration Based on Depth Estimation and Transmission Compensation

Underwater image restoration by red-dark channel prior and point spread function deconvolution

Improving visibility and fidelity of underwater images using an adaptive restoration algorithm

Restoration of underwater vision using a two-phase regularization mechanism

An Improved Dark Channel-Based Algorithm for Underwater Image Restoration