# PCB Defect Images Super-Resolution Reconstruction Based on Improved SRGAN
###### Abstract  
Image super-resolution reconstruction technology can boost image resolution and aid in the discovery of PCB flaws. The traditional SRGAN algorithm produces reconstructed images with great realism, but it also has the disadvantages of insufficient feature information extraction ability, a large number of model parameters, as well as a lack of fine-grained image reconstruction impact. To that end, this paper proposes an SRGAN-based super-resolution reconstruction algorithm for PCB defect images that is the first to add a VIT network to the generation network to extend the perceptual field and improve the model's ability to extract high-frequency information. The high-frequency feature extraction module is then used to enhance the generator's extraction of high-frequency information from the feature map while reducing the complexity of the model network. Finally, the inverted residual module and VIT network are combined to form the discriminator's backbone network, which extracts and summarizes shallow features while synthesizing global features for higher-level features, allowing the discriminator effect to be achieved with less spatial complexity. On the test set, the improved algorithm increases the PSNR by 0.82 and the SSIM by 0.03, and the SRVIT algorithm's number of discriminator model parameters and model space size are decreased by 2.01 M and 7.5 MB, respectively, when compared to the SRGAN algorithm. Moreover, the improved PCB defect image super-resolution reconstruction algorithm not only enhances the image reconstruction effect but also lowers model space complexity.  
super-resolution reconstruction; SRGAN; PCB defects; deep learning +
Footnote †: journal: Medical  
been used in image reconstruction. It intended to build an end-to-end network model that nonlinearly maps pictures using a three-layer convolutional neural network. When compared to previous algorithms, the final high-resolution images generated by this algorithm are considerably improved in every way, but they still suffer from slower speed and higher computational cost. [PERSON] suggested the FSRCNN algorithm in the same year to address these issues [10], which optimized the SRCNN network structure to reduce training costs and speed up operations while maintaining good performance. [PERSON] introduced the DRRN [11] deep recursive residual network-based image reconstruction algorithm in 2017, which reduced network parameters and optimized network performance. In 2022, [PERSON] proposed a new Efficient Super-Resolution Transformer (ESRT) for SISR. ESRT is a hybrid model that consists of a lightweight CNN backbone (LCB) and a lightweight deformer backbone (LTB) [12]. In 2023, [PERSON] proposed a fully transformer-based depth map super-resolution network. The method incorporates a novel cross-attention mechanism that seamlessly and continuously guides color images into a depth upsampling process [13]. Following that, different deep learning network structures were applied to the field of super-resolution image reconstruction [14] with remarkable results.  
Influenced by the Generative Adversarial Network (GAN) [15], in 2017, [PERSON] introduced the SRGAN (Super-Resolution Generative Adversarial Network) algorithm, which combined generative adversarial networks and super-resolution domains [16]; this algorithm suggests a discriminative network and a generative network to train images together, as well as a perceptual loss function, to make the reconstructed graphic texture clearer and enhance image visual effects. The SRGAN algorithm, in contrast, has flaws such as inadequate feature extraction capability and a large number of model parameters. To address this issue, this paper improves the SRGAN network and suggests an efficient PCB defect image super-resolution reconstruction algorithm based on the principles of model perceptual field improvement and feature fusion to obtain higher resolution PCB defect images.