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SOME WAVELET PROCESSING FOR SCANNED ASTRONOMICAL IMAGES

This study explores the advantages of using multiwavelet processing for scanned astronomical images, overcoming the limitations of orthogonal wavelets. The new Alpert multiwavelet shows promising results for compression and denoising.

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SOME WAVELET PROCESSING FOR SCANNED ASTRONOMICAL IMAGES

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  1. SOME WAVELET PROCESSING FOR SCANNED ASTRONOMICAL IMAGES Vasil Kolev Institute of Information and Communication technologies Bulgaria, Sofia, BAS

  2. Some disadvantages of the orthogonal wavelets • The orthogonal wavelets(except Haar) are non-regular and nonsymmetric - lead to bad image processing; • Some Daubechies wavelets is strong dependence on image structures; • For local mixed object in the image – wavelet are not effective! • Astronomical images of SPP have large regions of uniform intensity - we can processing more than wavelet in parallel ! • The main advantage of multiwavelets – they possessed simultaneously - Compact and Short supports; - Orthogonality/Biorthogonality; - Symmetry/Antisymmetry; - Higher order approximation; • Many more details information than scaling wavelets • Extract of more time-dependent approximated information! • New Orthogonal multiwavelet is obtained – from Alpert functions;

  3. Scalar Wavelet Compression H – analysisscaling function G – analysis wavelet function - Decimation

  4. Vector Wavelet Compression for 3-levels Q – matrix prefilter ! H – analysismatrix scaling functions G – analysis matrix wavelet functions

  5. Orthogonal Conditions We considering the well-know orthogonal multiwavelets - GHM, SA4, CL, Alpert Performance is expressed by Compression Level (CR):

  6. Results

  7. Conclusions 1 • The 3-levels decomposition given us sufficiently quality (PSNR > 25 dB) for general test images withbiggest PSNR for SA4 multiwavelet; • The GHM multiwavelet - minimal quality with (PSNR = 22-25 dB); • For CR=64, more 8dB different of the GHM multiwavelet: • 3-level decomposition in insufficiently for estimation of minimal values (about 0); • 4-levels decomposition given us excellent quality (>30 dB) (minimal worse than CL and Alpert but better from SA4); • For images characterized by much nonsmooth variation and large regions of uniform intensity about image center (M45-4063.fits) can be obtained lossless compression (PSNR > 40 dB) ;

  8. Conclusions 2 • Multiwavelet image compression the compression performance is dependent on the effectiveness of the decorrelating transform employed; • Multiwavelet decomposition is plate – dependent; • For local mixed object in plate have large regions of uniform intensity – orthogonal multifilters are a suitable choice to astronomical image processing, Some characteristics for new Alpert multiwavelet; • Shortest support (2-taps) for both scaling and wavelet functions - in comparison with CL (3-taps) and SA4, GHM are 4-taps ; • biggest PSNR (for 3 level and 4-level for some plates) • Alpert multiwavelet is easy implemented with lifting scheme only (dyadic sums); • Alpert multiwavelet can be preferred for astronomical images compression or denoising of scanned photographical plates (SPP);

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