Real-Time Image Denoising and Reconstruction Using Generative Adversarial Networks (GANs)
This paper presents a real-time image denoising and reconstruction system based on a compact Generative Adversarial Network (GAN) designed for embedded systems and edge computing. The proposed model employs a [ generator and a PatchGAN discriminator, trained using hybrid loss function that combines L1, perceptual, and adversarial terms to balance pixel and perceptual realism. Evaluations were conducted on multiple datasets, including DIV2K, BSD68, SIDD, DND, RENOIR, and PolyU at noise levels (σ = 15, 25, 50). Quantitatively, the proposed model achieves an average PSNR of 32.8 dB and an SSIM of 0.88, which is higher by more than 3 dB on average than those of existing models, including DnCNN, FFDNet, and RIDNet. The ANOVA, Wilcoxon, and Friedman tests revealed the improvements were significant (p < 0.001). Moreover, the model can be inferred in less than 50 ms on the Jetson Nano and Raspberry Pi 4, confirming its applicability in real-time settings. At the same time, visual inspection and preference tests proved the high quality of perceptions and maintenance of texture. Overall, the proposed GAN is a powerful, efficient, and perception-guided real-time image recovery technique for practical computer vision applications.