Wiener reconstruction of undersampled imagery

Abstract

We derive a Fourier-domain Wiener filter for the reconstruction of undersampled imagery. The filter differs from previous implementations in that it permits adjustment of the trade-offs between sharpness of the reconstruction, noise amplification, and aliasing artifact suppression. Additionally, a net transfer function that characterizes the combined effects of the imaging system and the reconstruction process is derived. This net transfer function is valid for both unaliased and aliased spatial frequencies. The expression for the net transfer function is applicable to more general linear image sharpening algorithms.

Improving Scanning Electron Microscope Resolution for Near Planar Samples Through the Use of Image Restoration

“…This may also be the cause of the oscillations in the power spectrum curve as well as the distinct peak in that curve at around 20 nm. A similar peak has been observed in regularized optical images by Thurman and Fienup (2009) and seems to be linked to the selection of the regularization parameters.…”

Application of the general image-quality equation to aberrated imagery

“…The postprocessing for the aberrated imagery did not use a simple 3 × 3 sharpening kernel, as was used for the reference imagery. Instead, a Fourier-domain Wiener filter [16][17][18][19] with more degrees of freedom was used to enable higher-fidelity aberration compensation. Also, the Wiener filter automatically yields postprocessed imagery that is optimum in terms of the expected mean-squared error without requiring a human observer "in the loop."…”

“…Specifically, the Wiener filter used here was based on the formulation of Eq. (19) in [19], using filter parameters c n ¼ 0:2 and c a ¼ 5. The Wiener filter requires exact knowledge of the PSF (and thus, the aberrations) of the imaging system, as well as lessstringent statistical knowledge of both the object being imaged and the noise sources.…”

“…The Wiener filter requires exact knowledge of the PSF (and thus, the aberrations) of the imaging system, as well as lessstringent statistical knowledge of both the object being imaged and the noise sources. Reference [19] gives a method of estimating from an image the power spectra for the object Φ o and the noise Φ n , where the noise is assumed to be white (Φ n equals a constant) and Φ o has the form…”

Intelligent adaptive resampling technique for the processing of remotely sensed imagery

“…NN resampling does not alter the brightness values of the original image; however, is not visually appealing due to its blocky effects (Baboo and Devi 2010;Thurman and Fienup 2009). Bilinear (BL) is a simple twopoint linear interpolator which uses the two neighbouring points to produce a smoothing effect to the image; however, is not used in most applications as it alters the actual image radiance (Australian Geo-Portal 2012).…”