/*
* Copyright (c) 2011-2016, Peter Abeles. All Rights Reserved.
*
* This file is part of BoofCV (http://boofcv.org).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package boofcv.alg.denoise.wavelet;
import boofcv.alg.denoise.DenoiseWavelet;
import boofcv.alg.denoise.ShrinkThresholdRule;
import boofcv.alg.transform.wavelet.UtilWavelet;
import boofcv.struct.image.GrayF32;
/**
* <p>
* Classic algorithm for wavelet noise reduction by shrinkage with a universal threshold. Noise
* is reduced by applying a soft threshold to wavelet coefficients. A method is provided for
* automatically selecting a reasonable threshold based upon the coefficients statistics.
* </p>
*
* <p>
* D. Donoho and I. Johnstone, "Ideal spatial adaption via wavelet shrinkage," Biometrics, Vol. 81, 425-455, 1994
* </p>
*
* @author Peter Abeles
*/
public class DenoiseVisuShrink_F32 implements DenoiseWavelet<GrayF32> {
ShrinkThresholdRule<GrayF32> rule = new ShrinkThresholdSoft_F32();
/**
* Applies VisuShrink denoising to the provided multilevel wavelet transform using
* the provided threshold.
*
* @param transform Mult-level wavelet transform. Modified.
* @param numLevels Number of levels in the transform.
*/
@Override
public void denoise(GrayF32 transform , int numLevels ) {
int scale = UtilWavelet.computeScale(numLevels);
final int h = transform.height;
final int w = transform.width;
// width and height of scaling image
final int innerWidth = w/scale;
final int innerHeight = h/scale;
GrayF32 subbandHH = transform.subimage(w/2,h/2,w,h, null);
float sigma = UtilDenoiseWavelet.estimateNoiseStdDev(subbandHH,null);
float threshold = (float) UtilDenoiseWavelet.universalThreshold(subbandHH,sigma);
// apply same threshold to all wavelet coefficients
rule.process(transform.subimage(innerWidth,0,w,h, null),threshold);
rule.process(transform.subimage(0,innerHeight,innerWidth,h, null),threshold);
}
}