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isotropic non-linear diffusion smoothing (Perona-Malik)

Now in addition to the my previous thread regarding isotropic linear diffusion smoothing, I want to solve the non-linear version of it based on the Perona-Malik approach. Again we have the following formula:

image description (1)

, where D is not a constant but varies across the image domain. A popular choice is the Perona-Malik diffusivity which can be given as:

image description (2)

where λ is the contrast parameter. ∇u(x, y) is the gradient of the image at pixel (x, y). It seems that KAZE features embed this functionality, therefore I have a look at the source code. Specifically the formula (2) is implemented with the following function:

/* ************************************************************************* */
/**
 * @brief This function computes the Perona and Malik conductivity coefficient g2
 * g2 = 1 / (1 + dL^2 / k^2)
 * @param Lx First order image derivative in X-direction (horizontal)
 * @param Ly First order image derivative in Y-direction (vertical)
 * @param dst Output image
 * @param k Contrast factor parameter
 */
void pm_g2(const cv::Mat &Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {

    Size sz = Lx.size();
    dst.create(sz, Lx.type());
    float k2inv = 1.0f / (k * k);

    for(int y = 0; y < sz.height; y++) {
        const float *Lx_row = Lx.ptr<float>(y);
        const float *Ly_row = Ly.ptr<float>(y);
        float* dst_row = dst.ptr<float>(y);
        for(int x = 0; x < sz.width; x++) {
            dst_row[x] = 1.0f / (1.0f + ((Lx_row[x] * Lx_row[x] + Ly_row[x] * Ly_row[x]) * k2inv));
        }
    }
}

However, while I am trying to use it regarding to what @LBerger came up in the other thread I cannot get the correct output. What do I miss again?

isotropic non-linear diffusion smoothing (Perona-Malik)

image description (1)

, where D is not a constant but varies across the image domain. A popular choice is the Perona-Malik diffusivity which can be given as:

image description (2)

/* ************************************************************************* */
/**
 * @brief This function computes the Perona and Malik conductivity coefficient g2
 * g2 = 1 / (1 + dL^2 / k^2)
 * @param Lx First order image derivative in X-direction (horizontal)
 * @param Ly First order image derivative in Y-direction (vertical)
 * @param dst Output image
 * @param k Contrast factor parameter
 */
void pm_g2(const cv::Mat &Lx, const cv::Mat& Ly, cv::Mat& dst, float k) {

    Size sz = Lx.size();
    dst.create(sz, Lx.type());
    float k2inv = 1.0f / (k * k);

    for(int y = 0; y < sz.height; y++) {
        const float *Lx_row = Lx.ptr<float>(y);
        const float *Ly_row = Ly.ptr<float>(y);
        float* dst_row = dst.ptr<float>(y);
        for(int x = 0; x < sz.width; x++) {
            dst_row[x] = 1.0f / (1.0f + ((Lx_row[x] * Lx_row[x] + Ly_row[x] * Ly_row[x]) * k2inv));
        }
    }
}

However, while I am trying to use it regarding to what @LBerger came up in the other thread I cannot get the correct output. What do I miss again?

I see that the author apply some scalar non-linear diffusion step functionality which I do not really understand what it is about it, here is the function. I tried what @Guanta suggested in the other thread

I think in the evolution_ - Vector (KAZEFeatures.h) are the evolutions over time, so if you'd take the last element and then from that element's struct (TEvolution.h) the Lsmooth Mat should be the image which has been smoothed. To create the evolution_-vector you need to call Create_Nonlinear_Scale_Space() .

but it did not give a good output :-(.