# Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images
## 2 Related Work  
Color correction is also known as white balance in image processing. Many popular existing automatic white balance algorithms are based on low level image features due to their simple concept and surprising performance [23], such as the gray-world (GW) and white-patch (WP) methods. The GW method works under the assumption that, given an image with sufficient color variations, the average of reflectance of a scene is achromatic [19; 20]. The WP method, also known as the perfect reflector  
Figure 1: Color cast and restoration of underwater images: (**a**) and (**c**) show the raw images, (**b**) and (**d**) present the restored results obtained by the proposed method. Raw images are provided with the UIEB dataset [8].  
method, is based on the hypothesis that the brightest pixel in an image corresponds to an object point on a glossy or specular surface [20; 24] which reflects the full range of light that it captures [18]. Consequently, the color of the brightest pixel is considered as the color of the light source and the brightest pixel will be assigned as the reference white point for white balance. The dynamic-threshold (DT) method [20] is another simple white balance method. It finds reference white points around the mean chromaticity coordinate and takes the ratios of the maximum luminance value to the mean RGB values of the white points as the channel gains.  
Conventional white balance methods and histogram equalization methods are often directly used to correct the color cast for underwater images [2; 7; 25; 26]. Considering the characteristics of underwater imaging, many specialized methods have been raised. To weaken the reddish cast produced by GW, an adaptive GW is developed by merging the global and local averages [27]. Two variants of shades of grey (SG) method [28; 29] are also adopted to efficiently implement the color correction. [PERSON] et al. [30] uses a binary depth map obtained by applying DCP [5] to GB channels to estimate multiple channel gains for different regions in underwater image. [PERSON] et al. [31; 32] blends the raw image with its color transfer image according to a weight map reflecting the desired level of correction. [PERSON] et al. [33] developed a learning-based weakly supervised color transfer model. [PERSON] et al. [34] corrects the regional or full-extent color deviation via frequency-based color-tone estimation. [PERSON] et al. [35] apply local surface reflectance statistical prior to the Retinex image formation model. A statistical-based (SB) method [21; 36] is applied to compress the outliners as well as expand the mid-range, which has the effect of image enhancement due to the expanded dynamic range.  
Most of these methods process RGB color channels separately, which is prone to introduce artifacts especially for underwater images due to the uneven attenuations in the three channels. Implementing color correction in color space other than RGB space is a good solution to this problem. [PERSON] et al. [37] performs the GW hypothesis in Ruderman opponent color space of \(\mathrm{L}\alpha\beta\) to correct the color of underwater images. [PERSON] et al. [38] proposed a chromatic adaptation based water-type dependent white balance (WTDWB) method which divides underwater images into three categories including blue-dominated, turquoise and green-dominated images. The illuminations of the turquoise and the green-dominated images are respectively estimated by applying WP and GW on the nonlinear RGB images. For blue-dominated images, white balance is not applied to inhibit the introduction of artifacts [38].