Research group
Superresolution and computational imaging
Information Optics Department
Superresolving imaging is a term that encompasses optical techniques of image aquisition and transmission with better resolution than could be expected from the characteristics of the imaging setup or in some cicumstances also better than predicted by the diffraction limit.
One of the methods of superresolving imaging is to use a medium with an increased refractive index. Going further, the same is possible with specially designed synthetic nanostructured materials such as metaldielectric layered materials. Such structers are one of the points of interest of our group.
Computational ghost imaging includes indirect imaging techniques. With this approach, we measure a different physical quantity or kind of data than we are finaly interested in. At the same time, the optical signal is subject to modulation during its aquisition. Then the measurement has to be recovered through computationaly demanding digital processing by solving an inverse problem. Image reconstruction techniques based on the theory of compressive sensing often allow for solving an incomplete inverse problem with ambiguous solutions. These methods make use of an a priori unknown internal structure of the data together with the assumption of the compressibility of the measured data. Intensive research is going on to use computational imaging and singlepixel detection for hyperspectral imaging, polarimetric imaging, 3D imaging, imaging through scattering media, imaging in IR anfd THZ ranges, or behindthecorner imaging.
Our current subject of interest related to the NCNOpus project "Superresolution hidden in the farfield and spatialspectral transformations" is to use an indirect measurement of the image spectrum in the farfield for recovering the information on nearfield nanostructures in a situation when the direct microscopic measurement is not possible due to the diffraction limit. To make it realistic it is necesary to introduce a dispersive spatialspectral mixing in the nearfield into the measurement.
The following are our recent publications on singlepixel imaging:
 Pastuszczak A., Stojek R., Wróbel P. and Kotynski R., 2021: Differential realtime singlepixel imaging with Fourier domain regularization  applications to VISIR imaging and polarization imaging, Optics Express, vol. 29(17) , pp. 2668526700 , 10.1364/OE.433199
 Bancerek M., Czajkowski K.M. and Kotyński R.: Farfield signature of subwavelength microscopic objects, Optics Express, vol. 28(24) , pp. 3620636218 , 2020 10.1364/OE.410240
 Czajkowski K., Pastuszczak A., and Kotyński R., 2019, Singlepixel imaging with sampling distributed over simplex vertices, Optics Letters, vol. 44(5), pp. 12411244, 10.1364/OL.44.001241
 Czajkowski K.M., Pastuszczak A., Kotyński R., 2018: Realtime singlepixel video imaging with Fourier domain regularization, Optics Express, vol. 26 , pp. 2000920022 , 10.1364/OE.26.020009
 Czajkowski K., Pastuszczak A. and Kotyński R., 2018: Singlepixel imaging with Morlet wavelet correlated random patterns, Scientific Reports, vol. 8, art. 466 , 10.1038/s41598017189686
We have introduced a Fourier domain regularization of the inverse problem occuring in singlepixel detection, with which the singlepixel camera may work in realtime. We have also demonstrated experimental results at the frequency of 11 Hz with 256x256 resolution. For those interested, we have released the FDRI Matlab/Octave package under the GNU license.
Published software (GNU license):

https://github.com/rkotynski/D_FDRI DFDRI  an efficient, fast, singlestep reconstruction method for singlepixel imaging (A. Pastuszczak, R. Stojek, P. Wróbel, R. Kotyński), 2021

https://github.com/rkotynski/RDFT Restricted domain Fourier Transform for Matlab/Octave/Python (M. Bancerek, K. Czajkowski, R. Kotyński), 2020

https://github.com/KMCzajkowski/FDRIsinglepixelimaging oraz FDRI. Fourier Domain Regularized Inversion (K. M. Czajkowski, A. Pastuszczak, and R. Kotyński), 2019
Research project

Superresolution hidden in the farfield and spatialspectral transformations
Source of financing: NCN, OPUS
Realisation period: June 29, 2018–June 28, 2022

Optical singlepixel detection based on the theory of compressive sensing
Source of financing: NCN, OPUS
Realisation period: July 8, 2015–Jan. 7, 2018

Plasmonic metamaterial  linear subdiffraction spatial filtering
Source of financing: NCN, OPUS
Realisation period: Dec. 1, 2011–Nov. 30, 2013
 Pastuszczak A., Stojek R., Wróbel P. and Kotynski R., 2021, Differential realtime singlepixel imaging with Fourier domain regularization  applications to VISIR imaging and polarization imaging, Optics Express, vol. 29(17), pp. 2668526700, 10.1364/OE.433199
 Bancerek M., Czajkowski K.M. and Kotyński R., 2020, Farfield signature of subwavelength microscopic objects, Optics Express, vol. 28(24), pp. 3620636218, 10.1364/OE.410240
 Czajkowski K., Pastuszczak A., and Kotyński R., 2019, Singlepixel imaging with sampling distributed over simplex vertices, Optics Letters, vol. 44(5), pp. 12411244, 10.1364/OL.44.001241
 Czajkowski K., Pastuszczak A. and Kotyński R., 2018, Singlepixel imaging with Morlet wavelet correlated random patterns, Scientific Reports, vol. 8, pp. art. 466, 10.1038/s41598017189686
 Czajkowski K.M., Pastuszczak A., Kotyński R., 2018, Realtime singlepixel video imaging with Fourier domain regularization, Optics Express, vol. 26(16), pp. 2000920022, 10.1364/OE.26.020009
 PastorCalle D., Pastuszczak A., Mikołajczyk M., Kotyński R., 2017, Compressive phaseonly filtering at extreme compression rates, Optics Communications, vol. 383, pp. 446452, 10.1016/j.optcom.2016.09.024
 Pastuszczak A., Szczygieł B., Mikołajczyk M., Kotynski R., 2016, Efficient adaptation of complexvalued noiselet sensing matrices for compressed singlepixel imaging, Applied Optics, vol. 55 (19), pp. 51415148, 10.1364/AO.55.005141
 Pastor D., Stefaniuk T., Wróbel P., ZapataRodriguez C., Kotyński R., 2015, Determination of the point spread function of layered metamaterials assisted with the blind deconvolution algorithm, Optical and Quantum Electronics, vol. 47 (1), pp. 1726, 10.1007/s1108201400104
 Pastuszczak A., Stolarek M., Antosiewicz T.J., Kotyński R., 2015, Multilayer metamaterial absorbers inspired by perfectly matched layers, Optical and Quantum Electronics, vol. 47 (1), pp. 8997, 10.1007/s110820149986z
 Pastuszczak A., Kotyński R., 2011, Optimized lowloss multilayers for imaging with subwavelength resolution in the visible wavelength range, Journal of Applied Physics, vol. 109, pp. art. 084302, 10.1063/1.3573479
dr hab. Rafał Kotyński 

mgr Krzysztof Czajkowski 

dr inż. Anna Pastuszczak 

mgr Rafał Stojek 

mgr Andrzej Janaszek 
