# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 3 Methods
### Chrominance Restoration Branch (CRB)  
The CRB is responsible for recovering the color information (Cb and Cr channels) of hydraulic concrete images, which is essential for restoring visual realism and preserving defect-related chromatic cues. As shown in Figure 1, the CRB is primarily composed of two core modules: the CDB and the FDDEB.  
Inspired by the work in [42] and as illustrated in Figure 5, we design the CDB with MHSA as its core component, integrating convolutional and attention mechanisms to independently denoise the Cb and Cr channels. Specifically, each chroma channel is passed through the CDB, where it undergoes four convolutional layers, the first with a stride of 1 to preserve local information and the remaining three with strides of 2 to progressively down-sample and extract features at multiple spatial scales. This design enables the network to capture both fine and coarse chrominance representations. The multi-scale features are then processed through MHSA to model global dependencies. A residual connection is applied to maintain feature integrity, followed by an up-sampling operation to recover the original resolution, thus completing the channel-wise denoising process.  
The CDB independently denoises the Cb and Cr channels and leverages multi-scale feature extraction to effectively suppress chromatic noise while preserving the subtle chrominance details essential for detecting surface defects in low-light hydraulic concrete images.  
Inspired by [1], we design an FDDEB within the CRB, aiming to more effectively capture and preserve the structural and textural information critical to hydraulic concrete surface inspection. In particular, this design is tailored to address the challenges of low-contrast defect boundaries, fine cracks, and subtle surface variations that are commonly present in real-world hydraulic concrete images.  
As illustrated in Figure 6, within the FDDEB, denoised-merged chroma channel information \(\hat{C}_{DCDC}\), undergoes a three-level discrete wavelet transform (DWT) for progressive down-sampling, followed by a symmetrical three-level inverse discrete wavelet transform (IDWT) during up-sampling. This wavelet-based decomposition captures both  
Figure 5: The framework of the Channel Denoiser Block (CDB).  
low-frequency chrominance context and high-frequency detail features, enabling the model to retain multi-scale texture cues such as crack edges, rough surfaces, and defect contours that are often lost in conventional down-sampling operations.  
To further refine these decomposed features, each DWT level is processed through a Residual Channel Attention Block (RCAB) [45], which selectively enhances informative channels while suppressing irrelevant or noisy responses. These RCAB-processed features are then connected via residual links to their IDWT levels, ensuring effective gradient flow and feature reuse. After up-sampling, the fused multi-scale outputs are again refined by an RCAB module to produce the final chrominance-restored feature map, \(\hat{C}_{D-DE}\).  
This hierarchical design allows the network to emphasize defect-relevant high-frequency textures in the chroma channels, which are often critical for visual distinction between damaged and intact regions. The FDDEB achieves a fine balance between detail enhancement and chroma fidelity, thereby improving the interpretability of enhanced images.