colour_demosaicing.demosaicing_CFA_Bayer_Malvar2004#

colour_demosaicing.demosaicing_CFA_Bayer_Malvar2004(CFA: ArrayLike, pattern: Literal['RGGB', 'BGGR', 'GRBG', 'GBRG'] | str = 'RGGB') → NDArrayFloat[source]#

Return the demosaiced RGB colourspace array from given Bayer CFA using Malvar (2004) demosaicing algorithm.

Parameters:

  • CFA (ArrayLike) – Bayer CFA.

  • pattern (Literal['RGGB', 'BGGR', 'GRBG', 'GBRG'] | str) – Arrangement of the colour filters on the pixel array.

Returns:

RGB colourspace array.

Return type:

numpy.ndarray

Notes

  • The definition output is not clipped in range [0, 1] : this allows for direct HDRI image generation on Bayer CFA data and post demosaicing of the high dynamic range data as showcased in this Jupyter Notebook.

References

[MHCW04]

Examples

>>> CFA = np.array(
...     [
...         [0.30980393, 0.36078432, 0.30588236, 0.3764706],
...         [0.35686275, 0.39607844, 0.36078432, 0.40000001],
...     ]
... )
>>> demosaicing_CFA_Bayer_Malvar2004(CFA)
array([[[ 0.30980393,  0.31666668,  0.32941177],
        [ 0.33039216,  0.36078432,  0.38112746],
        [ 0.30588236,  0.32794118,  0.34877452],
        [ 0.36274511,  0.3764706 ,  0.38480393]],

       [[ 0.34828432,  0.35686275,  0.36568628],
        [ 0.35318628,  0.38186275,  0.39607844],
        [ 0.3379902 ,  0.36078432,  0.3754902 ],
        [ 0.37769609,  0.39558825,  0.40000001]]])
>>> CFA = np.array(
...     [
...         [0.3764706, 0.360784320, 0.40784314, 0.3764706],
...         [0.35686275, 0.30980393, 0.36078432, 0.29803923],
...     ]
... )
>>> demosaicing_CFA_Bayer_Malvar2004(CFA, "BGGR")
array([[[ 0.35539217,  0.37058825,  0.3764706 ],
        [ 0.34264707,  0.36078432,  0.37450981],
        [ 0.36568628,  0.39607844,  0.40784314],
        [ 0.36568629,  0.3764706 ,  0.3882353 ]],

       [[ 0.34411765,  0.35686275,  0.36200981],
        [ 0.30980393,  0.32990197,  0.34975491],
        [ 0.33039216,  0.36078432,  0.38063726],
        [ 0.29803923,  0.30441178,  0.31740197]]])