This research paper presents a comparative analysis of two popular deep learning architectures for image denoising: the Autoencoder and the DnCNN. The study examines their performance across various noise levels using quantitative metrics such as PSNR values. The Autoencoder approach enables image compression to distill features, while DnCNN learns noise mapping directly, potentially improving efficiency and accuracy. Results indicate that DnCNN excels at moderate noise levels, but both models’ effectiveness diminishes as noise increases significantly. The comparative analysis reveals the strengths and limitations of each method, providing insights for selecting the most appropriate technique based on noise type and intensity. These findings contribute to the development of more efficient algorithm design and denoising strategies based on deep learning. The research advances our understanding of digital image processing techniques, with potential applications in fields such as medical imaging, remote sensing, and computer vision, where high-quality image restoration is critical.