Development of Hybrid Exemplar based DLSRGAN model for Restoration of the Distorted Signals

This research focuses on the Development of a Hybrid Exemplar-based Deep Learning SRGAN Model for the restoration of distorted signals. Traditional signal restoration techniques often struggle with noise and distortion, leading to loss of critical information. The proposed model integrates Super-Resolution Generative Adversarial Networks (SRGAN) with exemplar-based method to enhance the quality and fidelity of degraded signals. By leveraging the adversarial training framework, the generator learns to produce high-resolution outputs while the discriminator ensures perceptual realism. Initial results indicate significant improvements in signal clarity and detail recovery, outperforming conventional methods in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE). This hybrid approach not only restores signals more effectively but also preserves essential features, making it a valuable tool for applications in telecommunications and audio processing. Future work will focus on optimizing the model for real-time applications and expanding its use across various types of signal degradation scenarios.