A dual-phase hybrid framework for real-time grayscale image denoising in structured noise - Witefee, Diyar Mohammed; Al-kharaz, Ali Abdulmunim (International Journal of Advances in Intelligent Informatics, 2025)
Image denoising is a substantial section in the preprocessing stage, especially in medical images. This study proposed a hybrid denoising model for salt-and-pepper removal in grayscale images. The framework uses a U-Net convolutional neural network, modified to perform preliminary denoising, and the Alternating Direction Method (ADM) to refine the structure iteratively. A corrupted pixel location is first determined using an adaptive thresholding scheme. The model is trained with a composite loss function that combines pixel-wise reconstruction accuracy (MSE) and perceptual similarity, as measured by the Structural Similarity Index (SSIM). Tests conducted on benchmarks (e.g., Kodak24, Set14, DIV2K, and TID2013) show that the proposed method surpasses traditional filters and state-of-the-art deep learning models, e.g., FFDNet and DnCNN. The quantitative results are Peak Signal-to-Noise Ratio (PSNR) 32.45 dB, SSIM 0.92 against 30 percent salt-and-pepper noise, and the average speed of inference is 6.2 ms, showing improvements over baseline approaches in performance and appearance. The main innovation is combining a noise-aware adaptive detection step with a specially designed U-Net framework and ADM-sided refinement, achieving better edge preservation and robustness to noise at any level. The framework displays a high potential for use in medical imaging, document recovery, and real-time surveillance.