Underwater Image Restoration Research Articles

Restoration and Enhancement of Underwater Images Based on Bright Channel Prior

This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively.

Turbidity Underwater Image Restoration Using Spectral Properties and Light Compensation

Turbidity Underwater Image Restoration Using Spectral Properties and Light Compensation

Restoration of images degraded by underwater turbulence using structure tensor oriented image quality (STOIQ) metric.

Recent advances in image processing for atmospheric propagation have provided a foundation for tackling the similar but perhaps more complex problem of underwater imaging, which is impaired by scattering and optical turbulence. As a result of these impairments underwater imagery suffers from excessive noise, blur, and distortion. Underwater turbulence impact on light propagation becomes critical at longer distances as well as near thermocline and mixing layers. In this work, we demonstrate a method for restoration of underwater images that are severely degraded by underwater turbulence. The key element of the approach is derivation of a structure tensor oriented image quality metric, which is subsequently incorporated into a lucky patch image processing framework. The utility of the proposed image quality measure guided by local edge strength and orientation is emphasized by comparing the restoration results to an unsuccessful restoration obtained with equivalent processing utilizing a standard isotropic metric. Advantages of the proposed approach versus three other state-of-the-art image restoration techniques are demonstrated using the data obtained in the laboratory water tank and in a natural environment underwater experiment. Quantitative comparison of the restoration results is performed via structural similarity index measure and normalized mutual information metric.

Automatic Red-Channel underwater image restoration

Underwater images typically exhibit color distortion and low contrast as a result of the exponential decay that light suffers as it travels. Moreover, colors associated to different wavelengths have different attenuation rates, being the red wavelength the one that attenuates the fastest. To restore underwater images, we propose a Red Channel method, where colors associated to short wavelengths are recovered, as expected for underwater images, leading to a recovery of the lost contrast. The Red Channel method can be interpreted as a variant of the Dark Channel method used for images degraded by the atmosphere when exposed to haze. Experimental results show that our technique handles gracefully artificially illuminated areas, and achieves a natural color correction and superior or equivalent visibility improvement when compared to other state-of-the-art methods.

Region-specialized underwater image restoration in inhomogeneous optical environments

Underwater optical imaging and image processing play an important role in oceanic scientific research and practical applications. However, as light is absorbed and scattered by the water which it passes through, the underwater image acquired is seriously degraded. The resolution and contrast are lowered by light scattering and color is distorted by the wavelength-selective absorption. In order to solve these problems, various artificial sources are widely used in underwater imaging systems. However, additional sources except the parallel background light will cause pollution to the underwater optical environments, making it inhomogeneous. In this condition, the popular image processing methods based on the assumption of the homogeneous ambient light are weak or even futile. Aimed to solve this problem, a region-specialized method for underwater image restoration is proposed in this paper. Region-varied changes in the ambient light are taken into consideration for dehazing and color compensation. In contrast to the references including the state of art methods for both underwater image enhancement and restoration, the enhanced clarity and color fidelity are obtained through visual evaluation. Moreover, the highest score of no-reference perceptual quality assessment is obtained by the proposed method as well.

Restoration and Enhancement of Underwater Image based on Wavelength Compensation and Image Dehazing Technique

Getting clear images in underwater environments is an important issue in ocean engineering . The quality of underwater images plays a important role in scientific world. Capturing images underwater is difficult, generally due to deflection and reflection of water particles, and color change due to light travelling in water with different wavelengths. Light dispersion and color transform result in contrast loss and color deviation in images acquired underwater. Restoration and Enhancement of an underwater object from an image distorted by moving water waves is a very challenging task. This paper proposes wavelength compensation and image dehazing technique to balance the color change and light scattering respectively. It also removes artificial light by using depth map technique. Water depth is estimated by background color. Color change compensation is done by residual energy ratio method. A new approach is presented in this paper. We make use of a special technique called wavelength compensation and dehazing technique along with the artificial light removal technique simultaneously to analyze the raw image sequences and recover the true object. We test our approach on both pretended and data of real world, separately. Such technique has wide applications to areas such.

Underwater pulsed laser range-gated imaging model and its effect on image degradation and restoration

The imaging of underwater objects illuminated by artificial light has been of long-standing interest to investigators working in oceanographic environments. Pulsed lasers together with range-gated technology have been widely used for underwater optical imaging applications. In order to describe the formation of underwater range-gated images, a pulsed laser underwater imaging model based on pulse spatial and temporal broadening is proposed. Experiments based on a self-assembled laser range-gated imaging system were implemented in our laboratory. Results show good agreements between experiments and simulations. Both results also confirm higher image contrast toward the tail region of the target-reflected light. Furthermore, experiments on underwater image blur and restoration are also implemented and show good image recovery results. The modulation transfer function-based restoration mechanism also implies a way to eliminate the blur effect caused by light forward scattering.

Restoring turbulence-degraded underwater images

Abstract : Underwater imaging is essential for a variety of scientific and military applications, such as optical communications, situational awareness, diver visibility, and mine detection. Unfortunately underwater imaging is severely impaired by scattering and optical turbulence associated with refraction index fluctuations. Because of the similarities between underwater and atmospheric image degradation, approaches taken to solve the underwater image restoration problem originate in atmospheric turbulence compensation algorithms. However, applying unmodified atmospheric techniques to underwater images fails because of the differences in the scale of the refraction index fluctuations.

Underwater Image Restoration by Turbulence Model based on Image Quality Evaluation

For the underwater degenerative image caused by obscure and noise, atmospheric turbulence model is utilized to construct the restoration model. By computing the underwater image quality function which is used to estimate the adaptive degradation model parameter, underwater image is restored through Wiener filters. The connection between image contrast and average gradients and model parameter is analyzed as well. The experiments show that the proposed method exerts powerful effort on restoration of the obscure underwater image caused by scattering. The proposed method has a better improvement on contrast and definition of underwater image than other common preprocessing methods and is especially suitable for further degradation model analysis and objective detection.

Comparison of point spread models for underwater image restoration

It is known that absorption and scattering properties of water are the main causes of blur in underwater images. With the knowledge of point spread function (PSF), the performance of underwater image restoration can be effectively enhanced, which will also extend the imaging range as well. The presented effort reviews several empirical PSF models and an imagery-derived approach based on image formation. Varied models are applied for blind deconvolution restoration, performance of which are compared and discussed. Models under comparison include the empirical models by Duntley, Voss, Wells, as well as the imagery-derived approach which can also provide adequate accuracy and flexibility for image restoration, as shown by experimental results.

Model-based restoration and reconstruction for underwater range-gated imaging

In order to improve underwater range-gated imagery quality, image restoration and super-resolution reconstruction techniques based on maximum a posteriori framework are combined with the point spread function (PSF) of the system. A model based on beam propagation considering time-dependent backscatter is established including the extreme attenuation properties of water and the diffraction limit of sensor for obtaining the PSF. The proposed method is applied for an underwater range-gated imaging system. Performance of the approach is compared with the traditional restoration and reconstruction algorithms. Objective image quality metrics are used to quantify the effectiveness of the restoration and reconstruction. Experimental results showed that the proposed method can effectively improve the resolution and quality of underwater range-gated imaging.

Estimation-based approach for underwater image restoration

For underwater image restoration, modeling of PSF relies on a priori knowledge of the inherent optical properties under water. But full knowledge-based restoration could be corrupted by the complexity and instability of the application environments. Concerned with representation of the backscattering noise of underwater image, this Letter proposes a simplified multilayer transfer model to formulate both the point spread function and the statistics of backscattering noise explicitly. Consequently, the parameters for image restoration are estimated from in situ measurement of the backscattering background without a priori knowledge of the inherent optical properties. The robust Wiener filter is applied to implement the restoration. Experimental results are presented.

Comparative study on several blind deconvolution algorithms applied to underwater image restoration

Although the use of blind deconvolution of image restoration is a widely known concept, only few reports have discussed in detail its application to solving problem of restoration of underwater range-gated laser images. A comparative study of underwater image restoration using the Richardson-Lucy algorithm, the least-squares algorithm, and the multiplicative iterative algorithm for blind deconvolution is presented. All the deconvolution approaches use denoised underwater images and Wells’ small angle approximation theory of derived point spread function as the initial object and degradation guess, respectively. Owing the underwater no-reference imaging environment, image quality judgment based on the blur metric method is incorporated in our comparison to determine the appropriate deconvolution iteration number for each algorithm, which objectively evaluates the image restoration results. The performance of the three algorithms applied to underwater image restoration is discussed and reported.

Underwater image restoration by means of blind deconvolution approach

Although the use of blind deconvolution of image restoration is a widely known concept, little literatures have discussed in detail its application in the problem of restoration of underwater range-gated laser images. With the knowledge of the point spread function (PSF) and modulation transfer function (MTF) of water, underwater images can be better restored or enhanced. We first review image degradation process and Wells’ small angle approximation theory, and then provide an image enhancement method for our underwater laser imaging system by blind deconvolution method based on small angle approximation. We also introduce a modified normalized mean square error (NMSE) method to validate the convergence of the blind deconvolution algorithm which is applied in our approach. The results of different initial guess of blind deconvolution are compared and discussed. Moreover, restoration results are obtained and discussed by intentionally changing the MTF parameters and using non-model-based PSF as the initial guess.

Self-Tuning Underwater Image Restoration

A self-tuning image restoration filter based on a simplified version of the Jaffe–McGlamery underwater image formation model is presented. Optimal values of the filter parameters are estimated automatically for each individual image by optimizing a quality criterion based on a global contrast measure. The simplified model is ideally suitable for diffuse-light imaging with limited backscatter, but qualitative tests show good performance in a variety of imaging conditions. In addition, quantitative tests with a large number of frames from six real mission videos indicate a substantial performance improvement when restoration is used as a preprocessor for a classifier detecting man-made objects in unconstrained subsea videos.

ROV based underwater blurred image restoration

In this paper, we present a method of ROV based image processing to restore underwater blurry images from the theory of light and image transmission in the sea. Computer is used to simulate the maximum detection range of the ROV under different water body conditions. The receiving irradiance of the video camera at different detection ranges is also calculated. The ROV’s detection performance under different water body conditions is given by simulation. We restore the underwater blurry images using the Wiener filter based on the simulation. The Wiener filter is shown to be a simple useful method for underwater image restoration in the ROV underwater experiments. We also present examples of restored images of an underwater standard target taken by the video camera in these experiments.

A probabilistic approach to the coupled reconstruction and restoration of underwater acoustic images

Describes a probabilistic technique for the coupled reconstruction and restoration of underwater acoustic images. The technique is founded on the physics of the image-formation process. Beamforming, a method widely applied in acoustic imaging, is used to build a range image from backscattered echoes, associated point by point with another type of information representing the reliability (or confidence) of such an image. Unfortunately, this kind of images is plagued by problems due to the nature of the signal and to the related sensing system. In the proposed algorithm, the range and confidence images are modeled as Markov random fields whose associated probability distributions are specified by a single energy function. This function has been designed to fully embed the physics of the acoustic image-formation process by modeling a priori knowledge of the acoustic system, the considered scene, and the noise-affecting measures and also by integrating reliability information to allow the coupled and simultaneous reconstruction and restoration of both images. Optimal (in the maximum a posteriori probability sense) estimates of the reconstructed range image map and the restored confidence image are obtained by minimizing the energy function using simulated annealing. Experimental results show the improvement of the processed images over those obtained by other methods performing separate reconstruction and restoration processes that disregard reliability information.

Markov-based methodology for the restoration of underwater acoustic images

This article describes a probabilistic technique for the restoration of underwater acoustic images that is based on the Markov random fields (MRFs) methodology. The beamforming is applied to rough acoustic data that derive from multibeam systems or acoustic cameras to build a three-dimensional (3D) map, that is associated point by point with the estimates of the reliability of such measures. Specifically, backscattered echoes that are received by a 2D array antenna are arranged to generate two images in which each pixel represents the distance (range) from the sensor plane and the confidence of the measures, respectively. Unfortunately, this kind of image is affected by several problems due to the nature of the signal and the related sensing system. In the proposed algorithm, the range and the confidence images are modeled as separate MRFs whose associated probability distributions embed knowledge of the acoustic system, of the considered scene, and of the noise affecting the measures. In particular, the confidence image is first restored and the result is used to reconstruct the 3D image to allow an active integration of the reliability information. Optimal (in the maximum a posteriori probability sense) estimates of the reconstructed 3D map and the restored confidence image are obtained by minimizing the energy functionals, using simulated annealing. Experimental results on synthetic and real images show the performance of the proposed approach. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 386–395, 1997