of alpha = 0.4, radius = 0.6. For a more blatant effect try alpha = 0.5, radius = 1.0 . Optimal estimation smoothing. (1.0 <= alpha <= 2.0) This type of filter applies a smoothing filter adaptively over optimal estimation filter, then some subtle edge enhancement. Note that using edge enhancement is only likely to be useful after one of the non-linear filters (alpha trimmed mean or optimal estimation filter) optimal estimation filter (alpha 1.2, radius 1.0), a pass of the median filter (alpha 0.5, radius 0.55), and possibly a pass of the edge enhancement filter. Several passes of the optimal estimation filter

effect try Alpha = 1.0, Radius = 1.0 . 674 CHAPTER 17. FILTERS 17.4. ENHANCE FILTERS Optimal estimation This type of filter applies a smoothing filter adaptively over the image. For each pixel the variance optimal estimation filter, then some subtle edge enhancement. Note that using edge enhancement is only likely to be useful after one of the non-linear filters (alpha trimmed mean or optimal estimation filter) in images (i.e. turning .gif files back into 24 bit files) you could try a pass of the optimal estimation filter (Alpha = 0.2, Radius = 1.0), a pass of the median filter (Alpha = 1.0, Radius = 0.55), and

algorithms). PARAMETERS NL Filter provides three different filters: Alpha Trimmed Mean, Optimal Estimation and Edge Enhancement. The Radius slider controls the strength of the filter. Alpha determines Trimmed Mean and set the Alpha to 0.5 when removing single noise spots in the image. • Optimal Estimation is very good for reducing dithering noise. Low Alpha values make the smoothing subtle and high p Us er ’ s M a n u a l E nha nce Fil t er s • Edge Enhancement is the opposite of Optimal Estimation. Edge Enhancement sharpens edges instead of blurring them. Radius controls the effectiveness of