_applied_  
_sciences_  
_Article_  
LPS-Net: Lightweight Parallel Strategy Network for  
Underwater Image Enhancement  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2,*()  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 3: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 4: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 5: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 6: [[https://www.mdpi.com/](https://www.mdpi.com/)]([https://www.mdpi.com/](https://www.mdpi.com/))  
problem. Thus, in one line of traditional UIE methods, physical priors [9; 10] were utilized to estimate the unknown transmission map and background light. However, these physical priors might not always hold true in complex underwater scenes, resulting in poor performance when physical priors are violated. Another line of traditional methods [11; 12; 13] directly modifies image pixel values to improve visual quality, regardless of the physical model. These methods usually rely on hand-crafted features, thus exhibiting poor generalization ability. In recent years, UIE methods based on Convolutional Neural Networks (CNNs) have achieved promising results [14; 15; 16; 17; 18]. For example, [PERSON] et al. [14] presented an underwater image enhancement network via medium transmission-guided multicolor space embedding, called Ucolor. [PERSON] et al. [15] used wavelet-enhanced learning units to build a UIE framework. However, these methods usually require complex module designs in order to remove mixed degradations directly, resulting in a large number of parameters. Furthermore, these methods often have high computational complexity. For example, it takes the Ucolor [14] method 443.85 GFLOPs to process a 720P image. The considerable number of computational operations and parameters associated with these methods pose challenges for their deployment in underwater equipment with limited power supply, processing capabilities, and memory capacity. Therefore, designing a lightweight and effective underwater image enhancement model has become a challenge. To address the above issues, we propose a Lightweight Parallel Strategy Network (LPS-Net) for underwater image enhancement. As shown in Figure 1, LPS-Net achieves state-of-the-art underwater image enhancement performance with very few parameters. Specifically, a Dual-Attention Enhancement Block (DAEB) and a Mirror Large Receptiveness Block (MLB) are proposed to, respectively, enhance color and restore detail in degraded images. Problem splitting can avoid the use of complex modules, as well as reduce the amount of computation and parameters. With the aim of achieving remarkable image color and detail rendering, we repeatedly employed these blocks on parallel branches at each stage of LPS-Net and designed a Gated Fusion Unit (GFU) to combine features from different branches. In water, different wavelengths of light have varying rates of attenuation, with red light attenuating the fastest and blue-green light attenuating the slowest. As a result, there  
Figure 1: **Left:** Compared with existing state-of-the-art methods, our method achieves the best performance on the T90 dataset using only 80.12 k parameters (1/35 of that of MFEF [19], 1/80 of the parameter number of PRW-Net [15], and 1/2000 of that of Ucolor [14]). The size of the stars represents the number of trainable parameters.