Impulse Noise Suppression in Color Images Using Median Filter and Deep Learning

Refining the quality of a noisy image is essential for many image applications. Various median filter variants have been introduced to suppress various noises, but they have their own limitations when detecting noise and restoring noise-free images. Denoising convolutional neural networks (DnCNNs), primarily developed for Gaussian noise removal, are influential nonlinear mapping models in image processing. After alterations in training data, they can be used to suppress other noise with outstanding results. This article suggests a frequency median filter method combined with deep learning for color images polluted by Salt and Pepper (SnP) noise. The analysis presented in this paper has primarily used a frequency median filter to suppress impulse noise wherein the restored value for the center pixel is evaluated by the frequency median rather than the traditional median. After which, the pretrained denoising convolutional neural network is hired to suppress the remaining noise and attain the output image finally. With a visual comparative study, simulation results on the taken test images show that the proposed method surpasses de-noising methods in terms of PSNR, SSIM, NMSE, Entropy, IEF, NCC, PCC and Running Time.