High-resolution construction images are essential for quality monitoring and progress management. Addressing the challenge that existing super-resolution models often struggle to balance reconstruction quality with real-time inference speed in complex construction environments, this paper proposes a super-resolution reconstruction model based on attention mechanisms and Generative Adversarial Networks (GANs). The generator captures fine details through a Multi-scale Shallow Feature Extraction (MSFE) module. The deep feature mapping module introduces the SORRDB (SFT-Octave-Residual in Residual Dense Block) as its fundamental unit. This module integrates SFT layers for dynamic spatial feature modulation and incorporates Octave Convolution to decouple high- and low-frequency information, thereby reducing computational redundancy, while scSE attention mechanisms further enhance high-level structural features. The discriminator integrates Swin Transformer to capture both local and global features, enhancing the quality and realism of generated images. Experimental results on the SODA dataset demonstrate that the model achieves superior performance with PSNR, SSIM, and LPIPS scores of 28.063 dB, 0.825, and 0.197, respectively. Crucially, the model achieves an inference speed of 32.0 FPS on an NVIDIA GeForce RTX 4070, meeting the requirements for real-time monitoring. Therefore, the proposed model not only significantly enhances the perceptual quality and structural integrity of construction images but also achieves an optimal balance between performance and efficiency, offering substantial benefits for intelligent construction monitoring.
Chen et al. (Tue,) studied this question.