It is of great importance in image restoration to remove noise while preserving and enhancing edges. This paper presents a spatial correlation thresholding scheme for image restoration. The dyadic wavelet transform that acts as a Canny edge detector is employed here to characterize the significant structures, which would be strongly correlated along the wavelet scales. A correlation function is defined as the multiplication of two adjacent wavelet subbands with a translation to maximize the mathematical expectation. In the correlation function, edge structures are more discriminable because they are amplified while noise being diluted. Unlike most of the traditional schemes that threshold directly the wavelet coefficients, the proposed scheme applies thresholding on the correlation function to better preserve edges while suppressing noise. A robust threshold is presented and the experiment shows that the proposed scheme outperforms the traditional thresholding schemes not only in SNR comparison but also in the edge preservation.
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