# Multi-Module Combination for Underwater Image Enhancement
## 2 Related Work  
Underwater image enhancement represents a critical research area in marine science and technology, with significant contributions from numerous scholars aimed at facilitating more efficient and accurate ocean exploration. **Contrast Enhancement**: [PERSON] et al. [7] proposed a contrast enhancement fusion algorithm based on non-subsampled shearlet transform (NSST) framework. The method conducts a multiscale decomposition of the source image, separating it into low-frequency components and high-frequency sub-bands across multiple directions. Drawing on characteristics of human visual perception, the research team innovatively categorizes the low-frequency components into visually salient and non-salient regions. They effectively enhance the global contrast of the image through a fusion strategy while better preserving the detailed features of different regions. [PERSON] et al. [8] developed an innovative framework for underwater image enhancement that integrates color correction with dual-range contrast optimization. Their approach employs a sophisticated bi-interval histogram processing technique, which automatically determines an optimal equalization threshold to preserve a natural appearance while preventing over-enhancement. Additionally, the method incorporates an adaptive S-curve transformation to selectively boost local contrast and accentuate fine image details, achieving balanced enhancement across various regions of underwater images. In another study, [PERSON] et al. [9] tackled the issue of image degradation by implementing color channel correction and detail-preserving contrast enhancement. They designed a specialized attenuation matrix to compensate for inferior color channels. **Image Fusion**: [PERSON] et al. [10] designed a novel, unified, and unsupervised end-to-end image network to address various image fusion challenges. [PERSON] et al. [11] introduced a fast unified image fusion network based on the Proportional Maintenance of Gradients and Intensities (PMGI), which reformulates the image fusion problem as one of maintaining the proportionality of texture and intensity in the source images to accomplish multiple image fusion tasks. [PERSON] et al. [12] developed a semantic-aware real-time image fusion network (SeFusion) that integrates the image fusion module with a semantic segmentation module. [PERSON] et al. [13] presented a multiple exposure image fusion method designed to merge bracketed exposure captures into a single image, enhancing details across a wide dynamic range. **Underwater Image Enhancement**: [PERSON] et al. [14] proposed a multi-module fusion image enhancement network that integrates the original weights of two images along with correlation weights during processing. [PERSON] et al. [15] proposed a dual-domain migration-based underwater image enhancement method, which achieves the robust migration of cross-domain features by constructing a progressive feature transformation mechanism from the airborne domain to the underwater domain. [PERSON] et al. [16] also achieved cross-domain adaptation from the aerial domain to the underwater domain by implement  
Figure 1: UMMC results for real and simulated underwater images. ing a multi-feature enhancement mechanism (MFEF). This method utilizes multipath input to extract diverse forms of rich features from multiple viewpoints. [PERSON] et al. [17] developed a weighted wavelet visual perception fusion-based underwater image enhancement technique, which produces high-quality underwater images by merging high-frequency and low-frequency components from images of varying scales. While deep learning solutions in existing research have achieved promising results, their performance often relies heavily on large-scale training datasets and substantial computational resources. Traditional methods, as a crucial component of underwater image enhancement research, warrant in-depth study.