# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 2 Related Works  
In this section, we present a comprehensive review of relevant research, focusing on three main aspects: techniques for restoring underwater images, approaches for enhancing underwater images, and methodologies based on deep learning. Underwater image-restoration technology estimates underwater imaging model parameters to obtain images before degradation. Underwater optical imaging-based [5; 6; 7] and polarization characteristics-based methods [8; 9] consider the complexity of the underwater environment by constructing a specialized underwater imaging system, which yields results close to the ground truth. However, the limitations of these methods must also be considered--underwater optical imaging systems are sophisticated and costly as they require sophisticated hardware and capture equipment to obtain murky underwater images. [PERSON] et al. [10] proposed the dark channel prior (DCP) for outdoor image dehazing. This is also applicable to underwater image processing since the degradation of images in foggy and underwater environments is caused by the irregular propagation of light in the medium, which share some similarities. Methods based on DCP have also been widely applied in underwater image processing. [PERSON] et al. [11] applied the idea of DCP to the red channel to improve the visibility of underwater images. [PERSON] et al. [12] utilized the histogram characteristics of images to process underwater images. [PERSON] et al. [13] considered the degradation mode of underwater images and proposed a variant of DCP (UDCP). However, the method is not effective in the presence of white objects or artificial light. [PERSON] et al. [14] used image ambiguity and underwater light attenuation to propose an underwater image-restoration method. [PERSON] et al. [15] used a color-line model to restore underwater images. However, since these recovery-based methods depend on subaquatic prior knowledge and specific model parameters, they may not be effective in certain water conditions. [PERSON] [16] proposed a universal single-image-restoration algorithm by extending the dark channel prior (GDCP) to achieve restoration for different types of images; the method also demonstrated good performance in processing underwater images. The enhancement algorithm enhances an image's quality by adjusting its pixels' intensity. [PERSON] et al. [17] proposed an adaptive color-correction method for underwater images, where clustering algorithms are used to obtain the illuminant chromaticity of the image and then a linear color transformation is applied to correct the color distortion. [PERSON] et al. [18] fused two versions of images to enhance underwater images and videos. [PERSON] et al. [19] proposed a Retinex-based method for correcting color and enhancing the contrast in underwater images using different approaches to enhance the two components of underwater images. They also proposed a two-step enhancement method [20] for single underwater images, using DCP to remove the color cast and then using histogram equalization and the Retinex algorithm to enhance the image details. [PERSON] et al. [21] utilized dual-interval histograms to improve the quality of underwater images based on color correction. [PERSON] et al. [22] proposed the minimum color loss theory, which combines underwater image attenuation maps to adjust the color and contrast of the image. [PERSON] et al. [23] designed a matrix for color correction and then used histogram technology to enhance the contrast of underwater images. These enhancement-based methods are generally more stable and effective at improving the contrast and clarity of underwater images. Deep learning-based [24] underwater image-restoration and -enhancement methods have made significant progress in recent years, providing new possibilities for solving image quality problems in underwater environments. The method based on generative adversarial networks (GAN) performs well.