awsraw                package:adimpro                R Documentation

_S_m_o_o_t_h_i_n_g _a_n_d _d_e_m_o_s_a_i_c_i_n_g _o_f _R_A_W _i_m_a_g_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     The function integrates smoothing and demosaicing of RAW image
     data.

_U_s_a_g_e:

     awsraw(object, hmax = 4, aws = TRUE, wb = c(1, 1, 1), cspace = "Adobe", ladjust = 1, maxrange=TRUE,
     lkern = "Triangle", graph = FALSE, max.pixel = 400, compress = TRUE)

_A_r_g_u_m_e_n_t_s:

  object: an object of class 'adimpro' containing the RAW image data. 
          See 'read.raw' for creating such objects.  

    hmax: maximal bandwidth to use in the smoothing algorithm.

     aws: use adaptive weights if 'aws==TRUE'.

      wb: Vector containing factors for the three color chanels, allows
          to change the white balance. 

  cspace: Color space of the result,  

 ladjust: Factor for the kritical value '\lambda'. Defaults to '1',
          smaller values  increase sensitivity but may result in
          isolated noisy pixel. Values larger than '1' give smoother up
          to cartoon like results.

maxrange: If TRUE increase range of values to maximum.

   lkern: Specifies the location kernel. Defaults to "Triangle", other
          choices are "Quadratic", "Cubic" and "Uniform". The use of
          "Triangle" corresponds to the Epanechnicov kernel 
          nonparametric kernel regression. 

   graph: (logical). If  'graph=TRUE' intermediate results are
          illustrated after each iteration step. Defaults to 'FALSE'. 

max.pixel: Maximum dimension of images for display if 'graph=TRUE'. If
          the true dimension is larger, the  images are downscaled for
          display. See also 'show.image'.

compress: logical, determines if image data are stored in raw-format. 

_D_e_t_a_i_l_s:

     Adaptive smoothing is performed on the original RAW data,
     restricting positive weights to pixel corresponding to the same
     color channel. Noise is assumed to have a variance  depending
     linearly on the mean. Weights are determined by weigthed distances
     between  color vectors. These color vectors are obtained by
     demosaicing that is applied to the smoothed RAW data after each
     iteration of the smoothing algorithm. The demosaicing algorithm is
      a 3D generalized median, see 'method="Median4"' in function
     'develop.raw'.

_V_a_l_u_e:

     Object of class '"adimpro"' 

    img : Contains the reconstructed image. 

     ni : Contains the sum of weights, i.e. 'trace(W_i)', in all grid
          points 'i'.

    ni0 : Contains the maximum sum of weights for an nonadaptive kernel
          estimate with the same bandwidth.

   hmax : Bandwidth used in the last iteration.

   call : The arguments of the function call.

varcoef : Estimated coefficients in the linear variance model for the
          color channels.

_A_u_t_h_o_r(_s):

     Karsten Tabelow tabelow@wias-berlin.de and Joerg Polzehl
     polzehl@wias-berlin.de

_R_e_f_e_r_e_n_c_e_s:

     Polzehl, J. and Tabelow, K. (2007). Adaptive smoothing of digital
     images, Journal of Statistical Software 19 (1).

_S_e_e _A_l_s_o:

     ' \code{read.raw},\code{awsimage}, \code{make.image},
     \code{show.image}, \code{clip.image} '

_E_x_a_m_p_l_e_s:

     ## Not run: demo(raw)

