September 18, 2012

Adaptive seismic multiple removal with complex wavelet (paper)

The November-December 2012 issue of Geophysics (Volume 77, Issue 6) features (at last) a recent work performed on model-based, adaptive multiple removal in seismic. The concept is illustrated on the figure to the left. Signal obtained from direct reflections of interest (blue) are mixing with other waves bouncing between layers (red). They look alike except for differential attenuation in the frequency domains, different slopes in CMP gathers. Those interested could have a look at the booklet Seismic multiple removal techniques: past, present and future by Eric J. (Dirk) Verschuur. Those more patient may want to waiting for Seismic Multiple Elimination Techniques, by the same author, which should be published in June 2013.As the problem is quite complex per se, hundred of papers have been devoted to multiple elimination techniques, since the January 1948 special issue of Geophysics. A common approach consists in first computing one or several approximate models of the multiple reflections, and then trying to adaptively substract the model from the data. Such techniques usually combine an adapted representation (Fourier, Radon, different breeds of wavelets) and a matching or separation technique. The one we finally published resides at one end of the representation/matching spectrum, to cope with the industrial partner requirements. A somewhat redundant complex wavelet tranform (and yes, combining a Morlet wavelet frame and the complex trace, so to say) and a very simple sliding window 1-tap adaptive filter estimation on the complex scalogram, to adapt and remove a template disturbance signal from the original seismic trace. Maybe not the most theoretically proven approach, but a decent, fancy blend of complex wavelets and adaptive filtering, and some industrialized code that works. And a milestone in a nice collaborative venture, especially with Sergi Ventosa, now at IPGP. And finally published (took 1.5 years). So here it is:

Sergi Ventosa, Sylvain Le Roy, Irène Huard, Antonio Pica, Hérald Rabeson, Patrice Ricarte, Laurent Duval

Abstract: Adaptive subtraction is a key element in predictive multiple-suppression methods. It minimizes misalignments and amplitude differences between modeled and actual multiples , and thus reduces multiple contamination in the dataset after subtraction. The challenge consists in attenuating multiples without distorting primaries, despite the high cross-correlation between their waveform. For this purpose, this complicated wide-band problem is decomposed into a set of more tractable narrow-band problems using a 1D complex wavelet frame. This decomposition enables a single-pass adaptive subtraction via single-sample (unary) complex Wiener filters, consistently estimated on overlapping windows in a complex wavelet transformed domain. Each unary filter compensates amplitude differences within its frequency support, and rectifies more robustly small and large misalignment errors through phase and integer delay corrections . This approach greatly simplifies the matching filter estimation and, despite its simplicity, compares promisingly with standard adaptive 2D methods, on both synthetic and field data.
The preprint version is available, with nice color figures, under the umbrella of Arxiv. Next in line: explore other ends of the matching/transform spectrum. Comments welcome.

September 10, 2012

What is a color?

Typically the kind of image that makes me (still) love image processing. So the "light green" spiraled stripelets have the same absolute color coordinates (yes, R,G,B) as the "light blue" ones. I have checked it with XnView.

There is still room for image processing algorithms that meet vision stimuli.

The two-penny philosophical question: do these two colors actually merge at the aliased warped end in the center? Indeed, the phenomenon is related to the scale of observation, as one may obseve by zooming in and see how the green and the blue reduce their perceptual distance. Funnily enough, the illusion also works with at least one color blind and image processing specialist colleague (Frédéric Morain-Nicolier @ Pixel Shaker) who has been kind enough to discuss these issues. So below is the thumbnail and its enlarged version. Of course, you cannot fully trust the present image renderer,. Try by yourself.

Akiyoshi Illusion page:

September 7, 2012

SIVA Conferences : concern fees (update)

Although conferences = concern fees (with the anagram equivalence) for a few organizers, they allow to grab a few call for papers to other conferences. Grabbed from EUSIPCO 2012, along with cfp1 and cfp2 (on time-frequency theory and applications), and after a couple of proofs from a finally painfully published paper in Geophysics, the recent opening of GRETSI 2013 website in Brest, France, provides a good opportunity to release the latest updates on Signal, Image and Volume Analysis (SIVA) Conferences:

Eurographics 2013 (Annual conference of the European Association for Computer Graphics) calls for papers on 21/09/2013. CVPR 2015 (IEEE Conference on Computer Vision and Pattern Recognition) is announced in Boston, Massachussetts, USA; ICIP 2016 (IEEE International Conference on Image Processing) in Phoenix, Arizona, USA; ICCV 2015 (International Conference on Computer Vision) in Santiago, Chile. 

One of the next target is ICASSP 2013 in Vancouver, Canada, with submission deadline on 19/11/2012. And many more at SIVA Conferences...

For those interested in writing time-frequency papers, two special issue call for papers:

SPOQ: norm ratio sparsity restoration

So SPOQ means, in Swedish, "Svenskt Pediatriskt Ortopediskt Qvalitetsregister "  (Swedish Pediatric  Orthopaedic  Quality regist...