Multiframe blind deconvolution and bispectrum processing of atmospherically degraded data: a comparison

Abstract

We analyze the quality of reconstructions obtained when using the multi-frame blind deconvolution (MFBD) algorithm and the bispectrum algorithm to reconstruct images from atmospherically-degraded data that are corrupted by detector noise. In particular, the quality of reconstructions is analyzed in terms of the fidelity of the estimated Fourier phase spectra. Both the biases and the mean square phase errors of the Fourier spectra estimates are calculated and analyzed. The reason that the comparison is made by looking at the Fourier phase spectra is because both the MFBD and bispectrum algorithms can estimate Fourier phase information from the image data itself without requiring knowledge of the system transfer function, and because Fourier phase plays a dominant role in image quality. Computer-simulated data is used for the comparison in order to be able to calculate true biases and mean square errors in the estimated Fourier phase spectra. For the parameters in this study, the bispectrum algorithm produced less-biased phase estimates in all cases than the MFBD algorithm. The MFBD algorithm produced mean square phase errors comparable to or lower than the bispectrum algorithm for good seeing and few data frames, while the converse is true for many data frames and poor seeing.

Block matching and Wiener filtering approach to optical turbulence mitigation and its application to simulated and real imagery with quantitative error analysis

“…One important class of turbulence mitigation algorithms is bispectral speckle imaging. [6][7][8][9][10][11][12][13][14][15][16] This method seeks to recover the ideal image in the Fourier domain, by estimating the magnitude and phase spectrum separately. The magnitude spectrum is obtained with an inverse filter, or pseudoinverse filter, based on the LE optical transfer function (OTF).…”

“…7,9,10,16 Another class of turbulence mitigation algorithms uses some form of dewarping, fusion, and then blind deconvolution. 8,[15][16][17][18][19][20][21] Other related methods can also be found in the literature. [22][23][24][25][26][27][28] With most of these methods, a motion-compensated temporal average of video frames is computed first.…”