Mixed Noise Suppression Using UNET and its Variants

Image denoising holds significant importance in the realm of image processing due to the potential distortions caused by environmental factors and technical problems. Consequently, it is logical to consider image denoising as a critical research domain as it aids in addressing various other image processing challenges. Although numerous techniques for image denoising have emerged in recent years, a majority of them primarily focus on restoring images afflicted by a single source of noise. In this study, the effectiveness of UNET and its variant in denoising facial images with mixed noises is examined. Furthermore, traditional filtering techniques are investigated for the purpose of comparison. The experimental results indicate the insufficiency of conventional filtering techniques in effectively mitigating mixed noise in facial images. Conversely, employing UNET-based architectures yields promising outcomes, characterized by facial images exhibiting commendable values of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Furthermore, the denoised images produced by employing the proposed residual attention UNET exhibit notable enhancements in terms of clarity and intricate details.