Multiple Model Guided Image Filter for Image Denoising

In this paper, a multiple model guided image filter (MMGIF) is proposed to eliminate the various additive noises in the measured image and maintain the tracking performance of the automatic target recognition (ATR). One of the guided image filter (GIF) model is a standard GIF (SGIF) for removing the white Gaussian noise, and the other model is a Laplacian GIF (LGIF) for impulse noise called salt-and-pepper noise which generally occurs in CCD camera images. Furthermore, in order to select the proper model of guided filter, the image noise identification method is also proposed in this paper. The proposed algorithm estimates image noise types using kurtosis, skewness, and normality of the noise distribution. The performance of the proposed algorithm is evaluated by several simulations in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF).

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