Underwater Image Restoration Research Articles

Underwater Image Enhancement Based on Histogram-Equalization Approximation Using Physics-Based Dichromatic Modeling.

This work proposes to develop an underwater image enhancement method based on histogram-equalization (HE) approximation using physics-based dichromatic modeling (PDM). Images captured underwater usually suffer from low contrast and color distortions due to light scattering and attenuation. The PDM describes the image formation process, which can be used to restore nature-degraded images, such as underwater images. However, it does not assure that the restored images have good contrast. Thus, we propose approximating the conventional HE based on the PDM to recover the color distortions of underwater images and enhance their contrast through convex optimization. Experimental results demonstrate the proposed method performs favorably against state-of-the-art underwater image restoration approaches.

Underwater Image Restoration via DCP and Yin–Yang Pair Optimization

Underwater image restoration is a challenging problem because light is attenuated by absorption and scattering in water, which can degrade the underwater image. To restore the underwater image and improve its contrast and color saturation, a novel algorithm based on the underwater dark channel prior is proposed in this paper. First of all, in order to reconstruct the transmission maps of the underwater image, the transmission maps of the blue and green channels are optimized by the proposed first-order and second-order total variational regularization. Then, an adaptive model is proposed to improve the first-order and second-order total variation. Finally, to solve the problem of the excessive attenuation of the red channel, the transmission map of the red channel is compensated by Yin–Yang pair optimization. The simulation results show that the proposed restored algorithm outperforms other approaches in terms of the visual effects, average gradient, spatial frequency, percentage of saturated pixels, underwater color image quality evaluation and evaluation metric.

FloodNet: Underwater image restoration based on residual dense learning

The efficiency of the underwater image restoration task is hindered due to the degradation factors like light scattering, color shift, suspended particles and haze occurring in the underwater environment. In recent years, different methods have relied on the underwater image formation model and deep learning techniques to restore the underwater image, but they tend to produce unnatural artifacts and reduced levels of sharpness. To address these challenges, in this paper, we propose FloodNet using residual dense learning with the objective of estimating restored underwater images from a wide variety of degraded underwater images. The proposed FloodNet architecture is a fully convolution neural network that comprises of three modules, namely, low-level feature extraction for extracting features from the degraded underwater image, residual dense blocks that are densely connected via skip-connections for hierarchical feature fusion and to improve the flow of gradient during back-propagation, and finally global feature fusion to adaptively utilize both local and global residual learning to obtain a restored underwater image. We conduct the quantitative and qualitative evaluations on paired and unpaired underwater image datasets, as well as the application and user study analysis. The results demonstrate superior performance of FloodNet against existing SOTA methods.

Underwater image restoration based on exponentiated mean local variance and extrinsic prior

Underwater image restoration based on exponentiated mean local variance and extrinsic prior

Underwater Image Enhancement Using Improved CNN Based Defogging

Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.

A Variational Model with Second-Order Laplacian for Underwater Image Restoration

A Variational Model with Second-Order Laplacian for Underwater Image Restoration

Multi-Turbidity Underwater Image Restoration Based on Neural Network and Polarization Imaging

Multi-Turbidity Underwater Image Restoration Based on Neural Network and Polarization Imaging

A novel underwater image restoration method based on decomposition network and physical imaging model

Underwater image restoration is one of the significant research in marine engineering and aquatic robotics. However, due to the propagation characteristics of light and the serious turbidity in underwater, the captured images often have chromatic aberration and scattering blur, which brings great challenges to the restoration of the raw image. In this paper, a revised underwater imaging model is proposed first, which reanalyzes the generation of background light from the atmosphere to the underwater and provides important support for underwater color correction. And then a network framework via the revised model is designed, which can decompose the captured image into different components corresponding to the revised model. The proposed network consists of a decomposition architecture with residual blocks that learns a complete separation of clear image and transmittance features. These two features are used along with the raw image to predict the background light. Finally, combining three constraints of the imaging model, the proposed framework can converge rapidly along the desired direction. By comparison with the performance of the state-of-the-art algorithms, the designed network shows excellent visibility and is capable of removing water on both synthetic and real-world images in different water types.

An underwater-imaging-model-inspired no-reference quality metric for images in multi-colored environments

The color of an underwater target is different under different lighting conditions and underwater environments. To date, no quality metric has been proposed for images of underwater targets in multi-colored environments. In this paper, we proposed a no-reference quality metric for images of underwater targets in multi-colored environments (QMICE). This metric is a weighted combination of colorfulness index, contrast index and sharpness index. The colorfulness index is used to measure the color loss caused by absorption. The contrast index and sharpness index are used to measure the blurring caused by scattering. The weighted coefficients of the three indexes are calculated by multiple linear regression (MLR). For the contrast index and sharpness index, we proposed a grayscale conversion method that can adaptively adjust the coefficients of red, green, and blue (RGB) values to enhance their generalization ability under multi-colored environments. During the calculation of weighted coefficients, the quality scores based on turbidity are regarded as the true quality scores. It is more reliable than subjective assessment scores. The experimental results show that compared with the leading underwater image quality metrics available in the literature, the proposed metric has the best correlation between the metric predictions and the true quality scores. More importantly, QMICE can also be used to process underwater images in real time and evaluate the performance of underwater image restoration algorithms.

Underwater image restoration by color compensation and color-line model

The images captured underwater suffer from low contrast and color distortion owing to the harsh underwater imaging environment. An underwater image restoration algorithm is introduced in this paper based on color compensation and color-line model. Firstly, color compensation method is proposed to compensate the attenuated color information and improve the accuracy of estimated color lines. Secondly, the relationship of three-channel transmission is derived from the association between the global background light and the inherent optical characteristics. Then, an underwater image local transmission optimization model is established in light of the color line law to acquire local transmission map. Finally, the restoration images are performed by inverting the form of the simplified imaging model. Experimental results show that our method is superior to other methods in subjective evaluation, objective evaluation, color accuracy tests and application tests. This method can obtain images with significantly enhanced visibility and higher color fidelity.

Periodic integration-based polarization differential imaging for underwater image restoration

Underwater image restoration is extensively used in many applications, such as ocean exploration and underwater rescue. With the development of polarization imaging technology, polarization differential imaging (PDI) has gradually become one of the most effective tools for image restoration in the underwater environment. To eliminate the interferences of inconsistent polarization direction and image noise in conventional PDI systems, we propose a new underwater image restoration method based on the periodic integration of polarization images. The method replaces one or two pairs of orthogonal polarization images with the integration of a series of polarization images in PDI system to achieve underwater image restoration. Firstly, we captured a series of polarization images in different polarization directions during a complete variation cycle of image intensity. Then, these polarization images are accumulated together, and the result is approximately regarded as the intensity integration of polarization light in the dimension of polarization direction. Finally, we can calculate the polarization degree of each pixel and obtain a clear polarization differential image. Compared with the existing PDI methods based on one or two pairs of orthogonal polarization images, both qualitative and quantitative experimental results show that the proposed method has better performance in texture enhancement and noise suppression.

Underwater image restoration via depth map and illumination estimation based on a single image.

For the enhancement process of underwater images taken in various water types, previous methods employ the simple image formation model, thus obtaining poor restoration results. Recently, a revised underwater image formation model (i.e., the Akkaynak-Treibitz model) has shown better robustness in underwater image restoration, but has drawn little attention due to its complexity. Herein, we develop a dehazing method utilizing the revised model, which depends on the scene depth map and a color correction method to eliminate color distortion. Specifically, we first design an underwater image depth estimation method to create the depth map. Subsequently, according to the depth value of each pixel, the backscatter is estimated and removed by the channel based on the revised model. Furthermore, we propose a color correction approach to adjust the global color distribution of the image automatically. Our method only uses a single underwater image as input to eliminate lightwave absorption and scattering influence. Compared with state-of-the-art methods, both subjective and objective experimental results show that our approach can be applied to various real-world underwater scenes and has better contrast and color.

Restoration of degraded underwater images with attenuation coefficients as a clue

Underwater image restoration methods require the knowledge of wideband attenuation coefficients per color channel. In this paper, a Generative adversarial network(GAN) framework is proposed for underwater image restoration using the attenuation coefficients as an input. Attenuation coefficients like atmospheric light and transmission map are also estimated in order to restore an underwater image. For atmospheric light estimation, a combination of Res-net and Dense-net architecture is modelled and making it a learning based Residual-Dense Network. Through literature it is evident that, the transmission map describes about the portion of the light that is not scattered and reaches the camera. Transmission map is generated using the underwater image formation model. Further, these attenuation coefficients are given as an input to the proposed GAN architecture for underwater image restoration. The GAN architecture is having a generator block and a discriminator block. The result is demonstrated on synthetic and real underwater data-set and then compared with state-of-the-art method.

An Extensive Literature Review on Underwater Image Colour Correction.

The topic of underwater (UW) image colour correction and restoration has gained significant scientific interest in the last couple of decades. There are a vast number of disciplines, from marine biology to archaeology, that can and need to utilise the true information of the UW environment. Based on that, a significant number of scientists have contributed to the topic of UW image colour correction and restoration. In this paper, we try to make an unbiased and extensive review of some of the most significant contributions from the last 15 years. After considering the optical properties of water, as well as light propagation and haze that is caused by it, the focus is on the different methods that exist in the literature. The criteria for which most of them were designed, as well as the quality evaluation used to measure their effectiveness, are underlined.

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.

Underwater image restoration via feature priors to estimate background light and optimized transmission map.

Underwater images frequently suffer from color casts and poor contrast, due to the absorption and scattering of light in water medium. To address these two degradation issues, we propose an underwater image restoration method based on feature priors inspired by underwater scene prior. Concretely, we first develop a robust model to estimate the background light according to feature priors of flatness, hue, and brightness, which can effectively relieve color distortion. Next, we compensate the red channel of color corrected image to revise the transmission map of it. Coupled with the structure-guided filter, the coarse transmission map is refined. The refined transmission map preserves the edge information while improving the contrast. Extensive experiments on diverse degradation scenes demonstrate that our method achieves superior performance against several state-of-the-art methods.

Underwater image restoration by structured light and flood light imaging.

An underwater optical imaging system is indispensable for many oceanic engineering tasks, yet still plagued by poor visibility conditions. The serious degradation of underwater image results from light scattering and absorption. Removal of the backscattered light is the focus issue of underwater imaging technology to improve the image visibility, particularly in turbid water. In this paper, we present an approach for underwater image recovery using structured light imaging and flood light imaging to compose a combined imaging model with which the backscatter component is completely offset. The convolutional image is obtained using the structured light scanning imaging mode where the backscatter intensity is proportional to that of the flood light image with an unknown scale parameter. An algorithm to refine the matching of the backscatter components of both the convolutional image and the flood light image is proposed. Thus, subtraction of both images gives rise the combined imaging model without the backscatter component. Consequently, image restoration is completed by employing the deconvolution process. Results of underwater experiments are given.

Underwater image restoration using deep encoder–decoder network with symmetric skip connections

Underwater image restoration using deep encoder–decoder network with symmetric skip connections

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

Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.