Image restoration from a sequence of random masks

Peng, Xiaopeng; Ruane, Garreth; Artusio‐Glimpse, Alexandra B.; Swartzlander, Grover A.

doi:10.1117/12.2086419

Cited by 1 publication

(2 citation statements)

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“…(1) Batch mode multi-frame blind deconvolution [25,30], where all the distorted observations are processed at the same time; and (2) Serial mode multi-frame blind deconvolution [26,31,32], where degraded inputs are sequentially processed. Compared with the batch mode, the serial approach is more memory efficient and can in principle be done at the same time as the image acquisition.…”

Section: System Estimation and Image Recoverymentioning

confidence: 99%

“…Solutions to Eq.18 are commonly solved using either: (1) batch mode optimization using the constrained conjugate gradient descent of cost function with respect to f and {h n } in an alternating manner [25,28,30]; and (2) serial mode optimization using the multiplicative updates [26,31], as is shown in the method we employed, outlined below in Algorithm 1,estimated image and PSF are updated respectively using multiplicative method in each iteration:…”

Section: System Estimation and Image Recoverymentioning

confidence: 99%

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Randomized Aperture Imaging

Peng,

Ruane,

Swartzlander

2016

Preprint

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Speckled images of a binary broad band light source (600-670 nm), generated by randomized reflections or transmissions, were used to reconstruct a binary image by use of multi-frame blind deconvolution algorithms. Craft store glitter was used as reflective elements. Another experiment used perforated foil. Also reported here are numerical models that afforded controlled tip-tilt and piston aberrations. These results suggest the potential importance of a poorly figured, randomly varying segmented imaging system.

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