799a21efa211907c19d3afdd611ac7d70774f462,skimage/restoration/_denoise.py,,_wavelet_threshold,#Any#Any#Any#Any#Any#,338

Before Change

        threshold = sigma**2 / np.sqrt(max(img.var() - sigma**2, 0))

    denoised_detail = [{key: pywt.threshold(level[key], value=threshold,
                       mode=mode) for key in level} for level in coeffs[1:]]
    denoised_coeffs = [coeffs[0]] + [d for d in denoised_detail]
    return pywt.waverecn(denoised_coeffs, wavelet)

After Change

        threshold = [{key: _bayes_thresh(level[key], var) for key in level}
                     for level in coeffs[1:]]

    if np.isscalar(threshold):
        // a single threshold for all coefficient arrays
        denoised_detail = [{key: pywt.threshold(level[key],
                                                value=threshold,
                                                mode=mode) for key in level}
                           for level in coeffs[-1]]
    else:
        // dict of unique threshold coefficients for each detail coeff. array
        denoised_detail = [{key: pywt.threshold(level[key],
                                                value=thresh[key],
                                                mode=mode) for key in level}
                           for thresh, level in zip(threshold, coeffs[-1])]
    denoised_coeffs = [coeffs[0]] + denoised_detail
    return pywt.waverecn(denoised_coeffs, wavelet)

Instances