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you can use sepFilter2D

static Mat RLTikh_deconvolution(Mat observed, Mat psf, int iterations) {

    Mat deconv = observed.clone();
    double mu = 0.01;

    // Iterate
    for (int i = 0; i < iterations; i++) {

            // Temporary matrix
            Mat ratio;
            sepFilter2D(deconv, ratio, deconv.depth(), psf, psf, Point(-1, -1), 0,
            BORDER_REFLECT);

            divide(observed, ratio, ratio);

            sepFilter2D(ratio, ratio, ratio.depth(), psf, psf, Point(-1, -1), 0,
            BORDER_REFLECT);

            // TV Regularization
            Mat denom;
            Laplacian(deconv, denom, deconv.depth(), 1, 1, 0, BORDER_REFLECT);
            denom = 1.0 - 2.0 * mu * denom;
            divide(ratio, denom, ratio);

            // Apply iteration on the estimate
            multiply(deconv, ratio, deconv);
    }

    return deconv;
}

you can use sepFilter2D with your 1d kernel:

static Mat RLTikh_deconvolution(Mat observed, Mat psf, int iterations) {

    Mat deconv = observed.clone();
    double mu = 0.01;

    // Iterate
    for (int i = 0; i < iterations; i++) {

            // Temporary matrix
            Mat ratio;
            sepFilter2D(deconv, ratio, deconv.depth(), psf, psf, Point(-1, -1), 0,
            BORDER_REFLECT);

            divide(observed, ratio, ratio);

            sepFilter2D(ratio, ratio, ratio.depth(), psf, psf, Point(-1, -1), 0,
            BORDER_REFLECT);

            // TV Regularization
            Mat denom;
            Laplacian(deconv, denom, deconv.depth(), 1, 1, 0, BORDER_REFLECT);
            denom = 1.0 - 2.0 * mu * denom;
            divide(ratio, denom, ratio);

            // Apply iteration on the estimate
            multiply(deconv, ratio, deconv);
    }

    return deconv;
}

you can use sepFilter2D with your 1d kernel:

I believe I could apply the kernel two times (in X direction and Y direction)

that's exactly, what it does.

static Mat RLTikh_deconvolution(Mat observed, Mat psf, int iterations) {

    Mat deconv = observed.clone();
    double mu = 0.01;

    // Iterate
    for (int i = 0; i < iterations; i++) {

            // Temporary matrix
            Mat ratio;
            sepFilter2D(deconv, ratio, deconv.depth(), psf, psf, Point(-1, -1), 0,
            BORDER_REFLECT);

            divide(observed, ratio, ratio);

            sepFilter2D(ratio, ratio, ratio.depth(), psf, psf, Point(-1, -1), 0,
            BORDER_REFLECT);

            // TV Regularization
            Mat denom;
            Laplacian(deconv, denom, deconv.depth(), 1, 1, 0, BORDER_REFLECT);
            denom = 1.0 - 2.0 * mu * denom;
            divide(ratio, denom, ratio);

            // Apply iteration on the estimate
            multiply(deconv, ratio, deconv);
    }

    return deconv;
}