Image denoising remains a critical challenge in multimedia processing, balancing noise removal with the preservation of structural details. This paper evaluates a bio-inspired denoising framework that integrates edge-aware preprocessing with diffusion models. By synthesizing textures, applying Gaussian noise, and leveraging Sobel edge detection, the proposed method is benchmarked against traditional Gaussian blur. Quantitative metrics (PSNR, SSIM) and qualitative analyses demonstrate that diffusion models outperform conventional techniques, particularly in preserving edges. The study highlights the synergy between biologically inspired preprocessing and modern generative models, offering insights into scalable denoising solutions for applications like medical imaging and satellite photography.