# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 1 Introduction  
Hydraulic concrete is a widely used construction material in hydraulic engineering that plays a crucial role in the development of infrastructure. Accurate analyses andassessments of hydraulic concrete properties are essential for ensuring the quality, safety, and durability of concrete structures [1]. Image-based analysis has emerged as a powerful tool for the non-destructive evaluation of hydraulic concrete, enabling the extraction of valuable information about its physical characteristics. Many hydraulic structures are located in mountainous regions, where the environment is complex and includes numerous structural elements that require detailed inspection [2]. The quality of hydraulic concrete images is significantly influenced by factors such as the lighting conditions, precipitation, haze, and other environmental elements, with these effects being particularly pronounced in such challenging natural settings. The inherent complexity of hydraulic concrete images is manifested in drastic variations in luminance, contrast, and color, which pose significant challenges for subsequent visual tasks such as defect detection, as shown in Figure 1. Therefore, it is crucial to explore efficient image enhancement techniques to meet the stringent demands for image quality and clarity in practical applications [3]. In recent years, numerous methods have emerged in the field of image enhancement, including techniques based on histogram equalization [4], methods grounded in Retinex theory [5], strategies within deep learning frameworks [6], and, more recently, transformer-based techniques [7]. However, most of these methods focus on enhancing images in general scenes, with relatively little research on specialized enhancement methods for hydraulic concrete images. As a result, when these methods are applied to hydraulic concrete images, further adaptation and optimization are often necessary to fully realize their potential. To address the unique challenges of hydraulic concrete image enhancement, we propose a Dual-Branch Luminance-Chrominance Attention Network, termed DBLCANet-HCIE. Our approach explicitly targets the texture-rich and defect-sensitive characteristics of hydraulic concrete surfaces. We first perform a detailed analysis of the color space properties of hydraulic concrete images and demonstrate the representational advantages of the YCbCr color space. Based on this, we decompose the enhancement process into two complementary subtasks: luminance adjustment and chrominance restoration. By incorporating hybrid attention mechanisms tailored to each branch and channel-wise denoising operations, our model effectively enhances low-light regions while preserving critical defect textures and structural details that are essential for downstream analysis. To support model training and evaluation, we construct a dedicated dataset of low-light hydraulic concrete images. Extensive experiments show that our method outperforms several state-of-the-art techniques across multiple image quality assessment metrics. Furthermore, in practical surface defect detection tasks on real hydraulic concrete structures, our enhanced images  
Figure 1: Images of a hydraulic concrete surface with poor luminance: (**a**) low light; (**b**) non-uniform luminance. yield a substantial improvement in detection accuracy compared to their unenhanced counterparts, validating the effectiveness of our design in real-world scenarios. Our main contributions are summarized as follows:  
1. We analyze the color space characteristics of low-light hydraulic concrete images and propose a novel Dual-Branch Luminance-Chrominance Attention Network (DBLCANet-HCIE) that is specifically designed to address the challenges of poor visibility and degraded texture in such images. The network jointly performs luminance adjustment and chrominance restoration to enhance both global contrast and fine structural details. 2. We design a Luminance Adjustment Branch (LAB) and a Chroma Restoration Branch (CRB).