Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.

Enhancing underwater image via color correction and Bi-interval contrast enhancement

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

Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multi-scale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods.

/pdf/underwater-image-enhancement-based-on-global-and-local-i1nwx2qbdj.pdf

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Color correction and adaptive contrast enhancement for underwater image enhancement

Underwater image restoration via backscatter pixel prior and color compensation

In order to solve the problem of image degradation in foggy weather, a single image defogging method based on a multi-scale retinex with color restoration (MSRCR) of multi-channel convolution (MC) is proposed. The whole defogging process mainly consists of four key parts: estimation of illumination components, guided filter operation, reconstruction of fog-free images, and white balance operation. First, the multi-scale Gaussian kernels are employed to extract precise features to estimate the illumination component. After that, the MSRCR method is applied to enhance the global contrast, detail information, and color restoration of the image. Second, the smoothing constraints of both illumination component and reflected component are considered together by using the guided filter twice, thus the enhanced image satisfies the smoothing constraint and the noise in the enhanced image is reduced. Third, the enhanced image by the MSRCR and the image processed by the secondary guided filter are fused by linear weighting to reconstruct the final fog-free image. Finally, in order to eliminate the influence of illumination on the color of the defogged image, the final defogged image is processed by white balance. The experimental results demonstrated that the proposed method can outperform state-of-the-art methods in both qualitative and quantitative comparisons.

/pdf/single-image-defogging-based-on-multi-channel-convolutional-5hm37adwcd.pdf

Single Image Defogging Based on Multi-Channel Convolutional MSRCR

Underwater image enhancement method via multi-feature prior fusion

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

The red spectrum is saliently attenuated due to the absorption and scattering properties of water. The acquired underwater images show severe color cast in underwater scenes. In this letter, we propose a novel color correction method for underwater images, which removes color cast on single pixels based on scene depth. The experimental results demonstrate that our approach can significantly improve the color effect and provide a correct input for the subsequent underwater image defogging methods.

Jingchun Zhou

Papers

Single Image Defogging Based on Multi-Channel Convolutional MSRCR

Underwater image restoration via backscatter pixel prior and color compensation

Underwater image enhancement method via multi-feature prior fusion

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

Auto Color Correction of Underwater Images Utilizing Depth Information