FRACTAL IMAGE CODING WITH DEEP IMAGE PRIOR AND TOTAL VARIATION REGULARIZATION FOR SALT-AND-PEPPERS NOISE REMOVAL

The removal of Salt-and-Pepper noise remains a persistent challenge in image restoration due to its sparse, non-Gaussian nature and the severe disruption it causes to fine structures. Traditional denoising algorithms often struggle to preserve image details under such extreme corruption. We propose a unified, unsupervised denoising framework that integrates Fractal Image Coding (FIC), Total Variation (TV) regularization, and the Deep Image Prior (DIP). FIC captures long-range self-similarity through affine transformations; TV enforces local smoothness and edge preservation; DIP exploits the inductive bias of untrained convolutional networks to reconstruct structured content without requiring external datasets. By jointly optimizing fractal codes, variational constraints, and implicit priors, our method effectively suppresses impulsive noise while maintaining structural fidelity. Experimental results demonstrate that the proposed approach outperforms classical and learning-based methods in both noise removal and perceptual quality. This work bridges model-based and learning-free paradigms, offering a robust and interpretable solution for impulsive noise removal.