In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift, low contrast, noise, and blurring issues. This paper aims to classify and compare some of the significant technologies in underwater image defogging, presenting a comprehensive picture of the current research landscape for researchers. First we analyze the reasons for degradation of underwater images and the underwater optical imaging model. Then we classify the underwater image defogging technologies into three categories, including image restoration approaches, image enhancement approaches, and deep learning approaches. Afterward, we present the objective evaluation metrics and analyze the state-of-the-art approaches. Finally, we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.

Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey

Color correction and enhancement for underwater images is challenging due to attenuation and scattering. The underwater images often have low visibility and suffer from color bias. This paper presents a novel color correction method based on color filter array (CFA) and an enhancement method based on Retinex with dense pixels and adaptive linear histogram transformation for degraded color-biased underwater images. For any digital image in the RGB space, which is captured by digital camera with CFA, their RGB values are dependent and coupled because of the interpolation process. So we try to compensate red channel attenuation of underwater degraded images from the green channel and blue channel. Retinex model has been widely used to efficiently handle low brightness and blurred images. The McCann Retinex (MR) method selects a spiral path for pixel comparison to estimate illumination. However, the simple path selection doesn’t include global light dark relationship of the whole image. So we design a scheme to gain much well-distributed and denser pixels to obtain more precise intensity of illumination. Besides, we design a piecewise linear function for histogram transform, which is adaptive to the whole RGB value. Experiments on a large number of underwater degraded images show that, the processed images by our method have clearer details and uniform visual effect for all channels in RGB color space and our method can also obtain good performance metrics.

/pdf/color-correction-based-on-cfa-and-enhancement-based-on-57ez9yxf4a.pdf

Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images

Underwater image restoration: A state‐of‐the‐art review

In this paper, a two-step image enhancement is presented. In the first step, color correction and underwater image quality enhancement are conducted if there are artifacts such as darkening, hazing and fogging. In the second step, the image resolution optimized in the previous step is enhanced using the convolutional neural network (CNN) with deep learning capability. The main reason behind the adoption of this two-step technique, which includes image quality enhancement and super-resolution, is the need for a robust strategy to visually improve underwater images at different depths and under diverse artifact conditions. The effectiveness and robustness of the real-time algorithm are satisfactory for various underwater images under different conditions, and several experiments have been undertaken for the two datasets of images. In both stages and for each of image datasets, the mean square error (MSE), peak signal to noise ratio (PSNR), and structural similarity (SSIM) evaluation measures were fulfilled. In addition, the low computational complexity and suitable outputs were obtained for different artifacts that represented divergent depths of water to achieve a real-time system. The super-resolution in the proposed structure for medium layers can offer a proper response. For this reason, time is also one of the major factors reported in the research. Applying this model to underwater imagery systems will yield more accurate and detailed information.

Real-time underwater image resolution enhancement using super-resolution with deep convolutional neural networks

Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends

Underwater images are susceptible to various distortions compared to images taken on land, due to the nature of the water environment. These images often suffer from diffraction, polarisation, absorption, scattering, colour loss and attenuation of light. Each part of the ocean will have its own sources of distortions, due to flickers caused by direct sunlight, marine snow, the fluorescence of biological objects, the presence of macroscopical organisms, loss of stability in divers, loss of light, artificial lighting and floating dust particles present in the water. There are numerous techniques and algorithms that may be used to restore these underwater images. This study reviews different algorithms and methods, developed in the past two decades, to give clearer ideas on the techniques present in the image restoration process, specifically for underwater images.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.0117

Review of underwater image restoration algorithms

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes.

Role of Restored Underwater Images in Underwater Imaging Applications

Underwater images often undergo distortions from scattering, absorption, colour loss, diffraction, polarisation, and varying attenuation depending on the light frequency, due to the water medium. This study answers problems associated with the recovery of underwater images, by developing novel depth map estimation which may be used as an intermediary step for underwater image restoration. The depth map is an important factor for the recovery of the underwater image, as it has been shown that proper estimation of depth, results in better restoration of the underwater image. The proposed algorithm estimates a depth map from a single view image, using blurriness and lighting information, obtained using a simple background neutralisation method. Results from the algorithm on selected raw underwater image data set have been compared with other algorithms and it has been shown that the proposed method gives Pearson coefficients in the range of 0.75–0.95, which are considerably higher and better log root mean square value compared to other methods in the literature. This suggests a better estimation of depth by the proposed method.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.1533

Jarina Raihan A

Papers

Review of underwater image restoration algorithms

Role of Restored Underwater Images in Underwater Imaging Applications

Depth estimation for underwater images from single view image