Image signal-to-noise ratio estimation using adaptive slope nearest-neighbourhood model

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Noise in images, and particularly in scanning electron microscope (SEM) images, are undesirable. A new noise reduction technique, based on Adaptive Slope Nearest Neighborhood (ASNN), is developed. We apply this technique to single image signal-o-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, while the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In a few cases involving images with different brightness and edges, this ASNN is found to deliver an optimum solution for SNR estimation problems. For different values of noise variance (NV), this ASNN has highest accuracy and less percentage estimation error. Being more robust with white noise, the new proposed technique estimator has efficiency that is significantly greater than the existing methods which are original near neighborhood (simple method), first order interpolation method and shape-preserving piecewise cubic hermite autoregressive moving average (SP2CHARMA). (174 words).

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