Novel Image Denoising Techniques Using AFMF

Abstract

Background: In this paper, we have proposed a new image-denoising approach, which is a hybrid technique using the self-organizing migration algorithm (SOMA) and adaptive frequency median filter (AFMF). Materials and Methods: The first step in this approach consists of applying (AFMF) to the noisy image in order to have the first version of the denoised image. This first version of the denoised image is considered a clean image, which is then used as an input of an image-denoising system based on SOMA. This denoising system is then applied for denoising the noisy image and then a final version of the denoised image can be obtained. This image denoising system based on SOMA has two inputs, which are the noisy image and the corresponding clean image. However, we have available only the noisy image, and for that reason, we have first applied the AFMF to the noisy image and then obtained the first version of the denoised image as the clean image. In order to improve this proposed denoising technique, we have replaced the denoising system based on SOMA with targeted image denoising (TID), which is a more recent denoising approach. The PSNR (peak-SNR) and SSIM (structural similarity) have been used for evaluating the performance of the image-denoising techniques proposed in this work. Results: The results obtained from the computations of PSNR and SSIM show the performance of these proposed image-denoising techniques. Conclusion: The results obtained from the computations of PSNR and SSIM show that the proposed image-denoising techniques outperform a number of image-denoising approaches existing in the literature and used here for our evaluation.