# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 1 Introduction  
The underwater image is a crucial means of understanding the underwater world, and it plays an essential role in underwater exploration, subaquatic (underwater) operations, and underwater target recognition. However, due to the selective absorption of light by water, underwater images lose their original color and exhibit a blue (green) hue, which reduces the clarity of the image [1]. Moreover, due to the complex scattering of the subaquatic medium, the image has a foggy effect that significantly reduces the clarity of the scene [2].  
In order to tackle these challenges, scholars have devised a range of algorithms aimed at restoring and enhancing underwater images with the goal of improving their overall quality.  
Image-restoration algorithms are built upon the foundations of underwater imaging models and can effectively handle degraded images. However, the effectiveness of these methods heavily depends on parameter estimation, and some methods require specialized underwater equipment, which increases costs. In recent years, some scholars have combined deep learning with underwater images, but this relies on synthesized underwater degraded image pairs and corresponding high-quality land images, often involving complex network architectures [3, 4].  
In contrast, underwater image-enhancement algorithms require less underwater-specific prior knowledge and aim to improve pixel quality by adjusting pixel intensities. Enhanced images exhibit higher contrast and richer detail, resulting in better visual effects. Common enhancement algorithms are predominantly based on spatial-domain- or fusion-based approaches.  
Spatial-domain-based underwater image-enhancement algorithms effectively enhance image contrast. However, they may introduce red color casts and noise since color correction and noise handling are not explicitly considered. Fusion-based methods can improve image quality by reducing noise and enhancing details. However, these methods require the acquisition of multiple fusion images and the design of suitable fusion weights; otherwise, they may not yield positive results [3].  
To address these issues, this paper introduces an innovative and reliable underwater image-enhancement algorithm. First, an approach for color correction utilizing histogram equalization is used to correct color distortions. Then, guided filtering is applied to decompose the V channel of the color-corrected image into low-frequency and high-frequency components. The low-frequency component is enhanced using a dual-interval histogram based on the benign separation threshold and a pair of complementary gamma functions. The two enhanced outputs of the low-frequency component are fused to enhance image contrast, and an enhancement function is designed to highlight image details. We summarize the innovation points of this article as follows:  
* Color compensation is performed by combining the local and mean differences between the attenuation and non-attenuation channels of underwater degraded images. Based on this, a histogram correction technique based on histogram equalization is used to further correct the color of underwater images.
* Generate low-frequency components with different contrasts using dual-interval histograms and fuse the two versions of low-frequency components to enhance image contrast. Propose a function to highlight the high-frequency components of the V channel.