A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images

Abstract

Institute of Electrical and Electronics Engineers (IEEE)Camarena Estruch, JG.; Gregori Gregori, V.; Sapena Piera, A. (2013) Abstract-Mixed impulsive and Gaussian noise reduction from digital colour images is a challenging task because it is necessary to appropriately process both types of noise that in turn need to be distinguished from the original image structures such as edges and details. Fuzzy theory is useful to build simple, efficient and effective solutions for this problem. In this paper, we propose a fuzzy method to reduce Gaussian and impulsive noise from colour images. Our method uses one only filtering operation: a weighted averaging. A fuzzy rule system is used to assign the weights in the averaging so that both noise types are reduced and image structures are preserved. We provide experimental results to show that the performance of the method is competitive with respect to state-of-the-art filters.

Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

“…In our experiments, various kernel functions were used to determine the weight in (6). The suitability of the following functions, whose graphs are presented in Fig.…”

“…5, was evaluated: Fig. 6 Dependence of the optimal PSNR obtained using the proposed RLSF on α and r parameters using the Gaussian and Epanechnikov kernels. The test images shown in Fig.…”

“…The suppression of this kind of noise is a challenging task, as filters designed to cope with the Gaussian noise are not able to remove the impulses and the techniques designed for the impulsive noise removal are ineffective in the elimination of the noise modeled by the Gaussian distribution [6,22,23,37,49,50].…”

Fast averaging peer group filter for the impulsive noise removal in color images

“…The Fast Modified Vector Median Filter (FMVMF) is based on the design of the VMF and is utilizing fuzzy similarity measures [76][77][78]. This approach has been further extended to improve its denoising properties using fuzzy metrics in [79][80][81][82][83].…”

Impulse-mowing anisotropic diffusion filter for image denoising

“…Such methods may not be effective when the image is corrupted by Gaussian noise, random-valued impulses, or their combination. There have been several other algorithms proposed to eliminate a mixture of impulse and Gaussian noise [18,19].…”