Th`ese Marouan BOUALI - Sites personnels de TELECOM ParisTech

68 3. Standard destriping techniques and application to MODIS
of the selected wavelet. If the analysing wavelet has enough vanishing moments, regular
areas will have small wavelet coefficients and the stripe noise will be isolated in the highest
resolution scales (figure 3.17b). On the contrary, if the number of vanishing moments is
weak, stripe noise will also affect wavelet coefficients of lower resolutions (figure 3.17a)
and therefore, wavelet destriping provides images with a weak ID index.
The facet filtering model proposed in [Rakwatin et al., 2007] is used over the histogram
matching techniques to process random stripes. Due to its hybrid aspect, it is not compared
to other techniques.
Filtering techniques provide results that are visually better than equalization methods because
the removal of stripes can be tuned with σ for band-pass frequency filtering or m for
wavelet thresholding. However, blurring and ringing artifacts introduced in the corrected
signal discards any further quantitative analysis based on radiometric values.
The limitations of standard techniques exposed in this chapter serve as a basis for
the development of a robust destriping technique. An optimal destriping algorithm should
satisfy the following requirements :
- Complete removal of stripe noise, whether periodic or random
- Minimization of the distortion introduced in the restored image
From the definition of the NR and ID indexes, an optimal destriping algorithm increases
NR while leaving ID close to 1. To achieve such results, we explore in the next chapter
the striping issue from a variational perspective.