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High-spatial-frequency noise

The main error sources are noise in the wavefront sensor measurement, imperfect wavefront correction due to the finite number of actuators and bandwidth error due to the finite time required to measure and correct the wavefront error. Other errors include errors in the telescope optics which are not corrected by the AO system (e.g. high frequency vibrations, high spatial frequency errors), scintillation and non-common path errors. The latter are wavefront errors introduced in the corrected beam after light has been extracted to the wavefront sensor. Since the wavefront sensor does not sense these errors they will not be corrected. Since the non-common path errors are usually static, they can be measured off-line and taken into account in the wavefront correction. [Pg.195]

The functions used to change the decay functions in FSD increase the noise level faster than do the polynomial functions used for spectral derivatives because exponential functions tend to increase more rapidly at high spatial frequencies than polynomials. As a consequence, the effect on the data at high spatial frequencies in the Fourier domain signal (the region where the SNR is the lowest) is most severe. The general rule is that as the FWHH is narrowed by a factor of 2, the SNR of the spectmm decreases by an order of magnitude. In practice, the... [Pg.242]

Two methods of deconvolution can be employed the standard deconvolution called DCV where the image Pc is convolved by the matrix M which describes the aperture transmission, the deconvolution called PSF to which we add a convolution of a gaussian of which the sigma is adapted to the spatial resolution (2.61 pixels SI in 40-75 keV energy band). This new convolution allows to obtain the most significant flux and to eliminate the high frequency noise [5]. [Pg.191]

A spatial frequency low-pass filter can reduce the high-frequency noise generated by the camera, and ensure that the subpixel interpolation algorithm can still work in cases where the particles in the image map are less than 2 pixels in diameter. Typical image to vector processing sequence is shown in Fignre 4-63. [Pg.242]

Here the spectral density functions of the true image and noise are R/(u, v) and R/ /(u, v), respectively. Note that at spatial frequencies where the signal-to-noise is very high, the ratio Rn(u, v)/Ri(u, v) approaches zero, and the Wiener filter reduces to the inverse filter. However, when the signal-to-noise ratio is very poor (i.e., v)/... [Pg.130]

A number of types of pattern noise must also be considered in the far infrared imaging performance of SIRTF Close to the plane of the Milky Way, structure in the emission of the Galaxy rather than photon noise will determine the faintest sources that can be measured. Li principle, infrared cirrus can have a similar effect at high latitudes. However, the cirrus has a smooth structure with few small-scale features (Gautier 1994 Cutri, private communication). At the spatial frequencies appropriate to the diffraction limit of SIRTF, it can be shown that cirrus will be largely resolved out and wiU not limit the performance away from bright cirrus clouds. [Pg.421]


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High frequencies

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