**Right:** A sample image from the T90 dataset and its enhanced results produced by different methods including our LPS-Net. are different degrees of degradation among the three channels of RGB images, but previous methods [20; 21; 22] have not adequately addressed the hidden visual hazards caused by such differences. To deal with such degradation differences in the R, G, and B channels, the network should capture the distribution of color features across different channels. To this end, the incoming features are divided into three bands in the proposed DAEB. Each band passes through an Instance Normalization (IN) module, which will reduce the differences among different bands. Equipped with subsequent Channel Attention (CA) [23] and Spatial Attention (SA) [24], DAEB is bestowed with the ability to enhance the color of different color channels adaptively and separately. To restore image details, a large receptive field in the UIE network is desired, since increasing the receptive field allows for more contextual information to be obtained. Inspired by the long-range modeling ability of ViTs [25; 26], we adopted large-kernel convolutions to expand the receptive field for improving model performance, as exemplified by the remarkable ConvNet [27], which employs \(7\times 7\) depthwise convolutions. Specifically, the proposed MLB uses four large-kernel convolutions with different kernel sizes. We conduct a series of ablation studies in Section 5.2 and empirically set the kernel sizes in the MLB as \(7\times 7\) and \(9\times 9\), respectively. Additionally, we constructed parallel branches using the DAEB and MLB as primary components, then applied the GFU to integrate features from different branches. Extensive experiments demonstrated that LPS-Net achieves state-of-the-art UIE performance while using only 80.12 k parameters. Our main contributions in this paper are summarized as follows:  
* We propose the DAEB to capture the distribution of color features across the R, G, and B channels. IN can reduce the difference between channels, and by employing CA and SA, the network is able to enhance the color of degraded images at different levels. * We introduce a novel block named the MLB to focus on texture information in degraded images. With the large receptive field of the mirror-designed convolutional kernel, we were able to obtain more contextual information to restore fine details. * Our LPS-Net needs only 80.12 k parameters, making it well-suited for underwater devices with limited memory capacity. Extensive experiments demonstrated that LPS-Net achieves state-of-the-art UIE performance in terms of visual quality and evaluative metrics. The remaining sections of this paper are organized as follows: Section 2 reviews the traditional and deep-learning-based UIE methods. Subsequently, in Section 3, we introduce the proposed LPS-Net. Section 4 presents comprehensive qualitative and quantitative evaluations, aiming at comparing the efficacy of the proposed method against state-of-the-art approaches. Section 5 provides ablation studies of each module within the network, while Section 6 sheds light on the identified limitations and drawbacks. Finally, Section 7 encompasses a concise summary of our work as well as expounds upon future avenues of research and broader implications.