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Multispectral imaging of ore minerals in optical microscopy

Université de Liège, GeomaC–MICA, Sart Tilman B52/3, 4000 Liege, Belgium

View larger version (21K): [in a new window]   FIG. 1. Typical normalized transmittance curves for RGB colour filters fitted on a triple CCD video camera.

View larger version (21K): [in a new window]   FIG. 2. Optical design of a prism for synchronous imaging of the red, green and blue channels with three individual CCD detectors.

View larger version (35K): [in a new window]   FIG. 3. Bayer filter designed for colour imaging with a single CCD array. One pixel out of two is fitted with a green filter (G) and one out of four with a blue (B) or a red filter (R).

View larger version (14K): [in a new window]   FIG. 4. Typical spectral sensitivity curve of a silicon CCD detector.

View larger version (90K): [in a new window]   FIG. 5. White reference image obtained with a 10x MS-PLAN objective lens at 438±10 m. The original image appears homogeneous to the human eye as it displays grey-level values between 180 and 206 (left). After stretching of the histogram (right), attenuations due to dust, interference patterns and optical aberrations are clearly perceptible which demonstrates the importance of a background correction for proper imaging of mineral spectra.

View larger version (9K): [in a new window]   FIG. 6. Plot of reflectance standards of 4.3%; 14.7%; 25.1% and 87.3%, respectively, at 550 nm against grey levels measured with a CCD sensor fitted with a 550±10 nm interference filter. The grey levels are average intensities from a 20x20 pixels region taken in the centre of the field of view. The quality of the regression demonstrates the perfect linearity of the CCD response under the chosen imaging conditions.

View larger version (18K): [in a new window]   FIG. 7. The calibration procedure for digital imaging aims to equalize the grey-level values of pixels corresponding to the same mineral species wherever they are located in the field of view. A line drawn through the image of a single crystal illustrates how the original values are distributed (a). The use of a time averaging of successive frames drastically reduces the punctual noise (b). The additional correction for uneven illumination and uneven pixel response leads to an almost constant rendering of the uniform mineral surface (c).

View larger version (135K): [in a new window]   FIG. 8. The superposition of pictures from the same scene but taken at a 200 nm interval in wavelength show a distinct geometrical shift of the order of a few pixels. This chromatic aberration can be reduced by means of an image-translation operation.

View larger version (1045K): [in a new window]   FIG. 9. Calibrated digital video images taken with interference filters at wavelengths of 438 nm, 591 nm and 692 nm (from left to right). The upper sequence pictures a copper sulphide paragenesis in Kipushi, the central sequence is taken on a stannite-bearing ore from Vernerov and the lower sequence is from the sulphide paragenesis of the Panasqueira tin-tungsten deposit. Variable reflectance intensities are clearly noticeable for minerals such as bornite (Bo), pyrite (Py), arsenopyrite (Apy), chalcopyrite (Cp) and stannite (Sta).

View larger version (12K): [in a new window]   FIG. 10. Reflectance values from a given mineral surface obey a reasonably multigaussian distribution as shown here for true colour intensities from a 20x20 pixels region in bornite from Kipushi.

View larger version (199K): [in a new window]   FIG. 11. Optical smearing at the interface between pyrite and sphalerite (cf. zoomed circle) is responsible for the appearance of virtual ‘chalcopyrite’ (‘Cp’) and ‘pyrrhotite’ (‘Po’) rims after image classification. This effect due to mixed pixels spectra (mixels) can be removed in practice through posterior filtering.