Automated Processing of LASCO Coronal Images: Spurious Point-Source-Filtering and Missing-Blocks Correction

We report on automated procedures for correcting the images of the LASCO coronagraph for i) spurious quasi-point-sources such as the impacts of cosmic rays, stars, and planets, and ii) the absence of signal due to transmission errors or dropouts, which results in blocks of missing information in the images. Correcting for these undesirable artifacts is mandatory for all quantitative works on the solar corona that require data inversion and/or long series of images, for instance. The nonlinear filtering of spike noise or point-like objects is based on mathematical morphology and implements the procedure opening by morphological reconstruction. However, a simple opening filter is applied whenever the fractional area of corrupted pixels exceeds 50 % of the original image. We describe different strategies for reconstructing the missing information blocks. In general, it is possible to implement the method of averaged neighbors using the two images obtained immediately before and after the corrupted image. For the other cases, and in particular when missing blocks overlapped in three images, we developed an original procedure of weighted interpolation along radial profiles from the center of the Sun that intercept the missing block(s). This procedure is also adequate for the saturated images of bright planets (such as Venus) that bleed along the neighboring pixels. Missing blocks in polarized images may generally be reconstructed using the associated unpolarized image of the same format. But in the case of overlapping missing blocks, we implemented our procedure of weighted interpolation. All tests performed on numerous LASCO-C2 images at various periods of solar activity ( i.e. varying complexity of the structure of the corona) demonstrate the excellent performance of these new procedures, with results vastly superior to the methods implemented so far in the pipeline-processing of the LASCO images.