# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 4 Experiments and Results
### Dataset  
We trained our model on the LOLv1 dataset [23] and a self-built low-light hydraulic concrete image dataset (LLHCID). The LOLv1 dataset, which is frequently utilized for low-light image enhancement methods, comprises 500 pairs of low-light and normal-light images, with 485 pairs designated for training purposes and the remaining 15 pairs for testing.  
In our research, we found that there is currently no publicly available dataset specifically designed for low-light hydraulic concrete image enhancement. To address this gap and rigorously validate the effectiveness of our proposed method, we constructed a low-light hydraulic concrete image dataset (LLHCID) through a combination of on-site image acquisition and post-processing techniques. The dataset consists of 2000 image pairs, each containing a low-light image and a corresponding bright-light image. Among them, 1600 pairs were used for training, 200 pairs were used for validation, and 200 pairs were used for testing. All images were resized to 600 \(\times\) 400 pixels using high-quality interpolation with Python V3.10's Pillow library and saved in JPEG format to balance detail preservation and input consistency for the model.  
All images in the LLHCID were of real-world hydraulic concrete structures located in Southwest China. The image acquisition was conducted using a Huawei Mate60 Pro smartphone--purchased from the official website of Huawei Technologies Co., Ltd. (Shenzhen, China)--mounted on a fixed tripod, with the aperture consistently set to f/2.0. The bright-light images were captured under controlled conditions, with ISO sensitivity ranging from 100 to 2000 and shutter speeds ranging from 1/125 to 1/60 s. The corresponding low-light images were captured at the same sites, using ISO sensitivity ranging from 50 to 80 and shutter speeds between 1/250 and 1/125 s, resulting in brightness levels that were approximately 20-60% of their bright-light counterparts. This acquisition protocol ensures significant luminance contrast while maintaining spatial alignment. Importantly,a large portion of the collected images contain visible surface defects, including cracks, spalling, and corrosion, making the dataset highly relevant for defect-aware enhancement and detection tasks.  
To ensure the reliability and diversity of the dataset, all image pairs were manually reviewed and aligned. Figure 8 presents representative samples from the LLHCID, showcasing a variety of paired low-light and bright-light hydraulic concrete images captured under different illumination conditions. The samples include diverse surface states, such as those free of defects and those with cracks, spalling, efflorescence, exposed aggregate, and erosion.  
We further analyzed the histogram distribution of low-light images in the YCbCr color space. The Y channel effectively reflects the overall luminance levels, while the Cb and Cr channels exhibit more concentrated pixel distributions in background regions. In contrast, defect regions tend to show distinguishable deviations in chrominance, allowing subtle defect textures to be better preserved and distinguished through chrominance restoration.  
We plan to publicly release part of the dataset in future work to support broader research in low-light enhancement and structural inspection of concrete surfaces.  
We used the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) as two indicators to quantitatively evaluate the performance of the model.