Can one say a little more? Of course, bibliometrics or scientometrics generate a lot of debates. Generally, such indicators do not mean anything absolute, they may only serve as a ground for discussion. Let us just compare the figures with the journal statistics: Signal Processing has a two-year Impact Factor on 1.851 (2012). Special issue citation data is tabulated in the following array:
One observes that the citation counts for Elsevier Scopus, ISI-Thomson Web of Science or Google Scholar are very much uneven. As usual, Google Scholar lies above the two others. This observation should suffice, at least for genuine data scientists, to refrain from using carelessly a single number such as the h-index, without citing the source. When a reality (one's paper visibility) is given three very different values by three similar sensors (with different vendors), one should be cautious about using only the sensor value she-he prefers. This attitude should be very uncoherent for people claiming they can denoise measurements, restore signals, analyze images with precise tools. And forget all about the scientific method when it comes to quantified self-performance.
Then the counts are very different for the different papers. So an average index (here 3.5, or 2.8 without the overview paper) is not meaningful. One potential sound approach is to resort to range statistics, with the least favorable index (ISI-Thomson-WoS). Four papers have not been cited yet. The seven others have a citation count [11, 7, 7, 5, 5, 2, 2] greater than the impact factor (1.851). Qualitatively, the performance of this special issue may be said a little above the journal's performance.
Of course, the eleven papers have a longer life ahead than a two-year run.The only thing we may wish is an absolute improvement of their visibility and influence. Meet you in December 2015, to see how the pack has grown. Here is the paper leaflet.
Keywords: Review; Multiscale; Geometric representations; Oriented decompositions; Scale-space; Wavelets; Atoms; Sparsity; Redundancy; Bases; Frames; Edges; Textures; Image processing; Haar wavelet; Non-Euclidean wavelets; Augmented Lagrangian methods; MRI reconstruction; Non-uniform Fourier transform; Shearlet; Compressed sensing; Model selection; White noise model; Image estimation; Geometrically regular functions; Bandlets; Dictionary; Matching pursuit; Shrinkage; Sparse representation; Lossless compression; Progressive reconstruction; Lifting schemes; Separable transforms; Non-separable transforms; Adaptive transforms; Multiresolution analysis; Wavelets; Stereo coding; Mono- and multivariate empirical mode decomposition; Filter bank structure; Electroencephalography data analysis; Sparse signal representation; Constant-Q transform; Wavelet transform; Morphological component analysis; BOLD fMRI; Hemodynamic response; Wavelet design; Sparsity; l1 minimization; Sparse approximation; Greedy algorithms; Shift invariance; Orthogonal Matching Pursuit; Multiscale decomposition; Sparse approximation; Time—frequency dictionary; Audio similarity; Wavelet transform; Frame; Symmetric filterbanks; Multiresolution analysis