Abstract

In this paper, an improved Spatially Adaptive Denoising Algorithm (SADA) is proposed which leads to satisfactory results in terms of objective and subjective when the image is corrupted with the SNR level less than 10dB of additive white Gaussian noise. In general, suppression of Gaussian noise poses a trade-off problem between denoising and preserving the detailed information of the image. So in this proposed method, the parameters of local statistics are used for effective noise suppression with preserving detailed information as compared to PWMAD, SAWM and SADA methods. All pixels including the diagonal elements of the local window with uniform weighting coefficients are taken in our proposed method for the noise detection and removal. Local statistics, computational cost, over-smoothness, error detection and smoothing degree of reconstructed image are the parameters taken into account to effectively remove the noise components in the proposed method.

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