Application of image reconstruction algorithm combining FCN and Pix2Pix in visual communication design

Abstract

This study addresses the limitations of traditional image reconstruction techniques in handling complex textures and fine details, which often result in poor image quality due to detail fidelity bottlenecks. An innovative image reconstruction framework is proposed by combining the global feature extraction capabilities of fully convolutional networks (FCNs) with the detail optimization advantages of the Pix2Pix generative adversarial network. The FCN’s fully convolutional architecture effectively captures the macro-structure of images, providing a solid foundation for reconstruction. The Pix2Pix network then generates preliminary results through adversarial learning, significantly enhancing image realism and texture naturalness. The performance of the proposed FCN-Pix2Pix framework was evaluated using the high-standard DIV2K dataset, which includes a variety of complex scenes. Compared to enhanced deep super-resolution network (EDSR) and super-resolution generative adversarial network (SRGAN), the FCN-Pix2Pix combination outperformed both in detail preservation and structural similarity. Specifically, the FCN-Pix2Pix model improved the peak signal-to-noise ratio (PSNR) by 3.2 and 4.0, and the structural similarity index (SSIM) by 0.03 and 0.06, respectively. The results demonstrate the significant advantages of this approach in maintaining image details and structural integrity. In conclusion, the integration of FCN and Pix2Pix not only addresses the challenges of image reconstruction in complex textured scenes but also significantly enhances image detail richness and texture quality, providing a reliable and efficient solution for high-quality image reconstruction in visual communication design.