ACmF for Salt and Pepper Noise Removal

Code of the paper titled "Adaptive Cesáro Mean Filter for Salt-and-Pepper Noise Removal"

Adaptive Cesáro Mean Filter for Salt-and-Pepper Noise Removal

Citation:

S. Enginoğlu, U. Erkan, and S. Memiş, 2020. Adaptive Cesáro Mean Filter for Salt-and-Pepper Noise Removal, El-Cezeri Journal of Science and Engineering, 7(1), 304-314. doi: https://doi.org/10.31202/ecjse.646359

Abstract:

In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions. We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database. Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above. Moreover, we also compare the running time data of these algorithms. These results show that ACmF outperforms the methods except for DAMF. We finally discuss the need for further research.