Improving Image Fidelity using Skip Connections Autoencoder

This study presents an advanced autoencoder model with skip connections designed to enhance the image fidelity quality. Traditional methods often face challenges in preserving high-resolution details and textures, particularly in tasks involving noise reduction, super-resolution, and low-light enhancement. Our model leverages skip connections to maintain essential spatial information across different layers, effectively addressing the vanishing gradient problem and improving gradient propagation during training. We compare our model against several state-of-the-art methods, including the Overall Improved Autoencoder (OIAE), Cascade Decoders-Based Autoencoders, and Edge-Aware Autoencoder Design, evaluating performance based on Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Our autoencoder consistently achieves superior results, with MSE of 5.889e-05, PSNR consistently above 40 dB, and SSIM close to 1. These findings demonstrate the significant advantages of incorporating skip connections, which allow for better retention of high-resolution features and more accurate image reconstructions.