[−][src]Function opencv::photo::fast_nl_means_denoising_multi_vec

pub fn fast_nl_means_denoising_multi_vec(
    src_imgs: &dyn ToInputArray, 
    dst: &mut dyn ToOutputArray, 
    img_to_denoise_index: i32, 
    temporal_window_size: i32, 
    h: &Vector<f32>, 
    template_window_size: i32, 
    search_window_size: i32, 
    norm_type: i32
) -> Result<()>

Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394

Parameters

  • srcImgs: Input 8-bit or 16-bit (only with NORM_L1) 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
  • imgToDenoiseIndex: Target image to denoise index in srcImgs sequence
  • temporalWindowSize: Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
  • dst: Output image with the same size and type as srcImgs images.
  • templateWindowSize: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
  • searchWindowSize: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
  • h: Array of parameters regulating filter strength, either one parameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
  • normType: Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1

C++ default parameters

  • template_window_size: 7
  • search_window_size: 21
  • norm_type: NORM_L2