<p>Salt-and-Pepper Noise(SPN) is one of the major challenges in image restoration, as it tends to severely degrade structural details and textures, particularly under high noise conditions. In this paper, we propose a novel image denoising method based on Cellular Automata(CA). The proposed method leverages simple local rules and the inherent ability of CA to preserve spatial continuity, allowing for the iterative removal of SPN while effectively maintaining the structural characteristics of the original image. Experimental results on standard benchmark datasets show that the proposed method achieves higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index Measure(SSIM) compared to existing techniques. Furthermore, histogram and entropy analyses confirm that the intrinsic features of the images are well preserved after denoising. The proposed approach demonstrates stable performance even under severe noise conditions, without relying on complex learning models or high computational cost, making it highly suitable for real-time image restoration and practical applications such as embedded systems.</p>