Bulgarian Academy of Sciences
Institute of Optical Materials and Technologies
"Acad. Jordan Malinowski"
Bulgarian Academy of Sciences
Institute of Optical Materials and Technologies
"Acad. Jordan Malinowski"
"Acad. Jordan Malinowski"
Acad. J. Malinowski (1923 - 1996)
Academician Jordan Malinowski was born on 03.06.1923 ...
Invitation
National projects
National Science Infrastructure
INFRAMAT
National Science Infrastructure
INFRAMAT
INFRAMAT
Most significant scientific achievement for 2025
Na₂S-assisted CVD synthesis of 2D WS₂ flakes, coordinator prof. Vera Marinova, DSc
WS2 in mono- and few-layer forms were prepared by the reaction of S with WO3 using substrate pretreatment with sodium sulfide (Na2S) as a source of H2S, providing an efficient, cheap and environmentally friendly synthesis method. The combination of XRD, atomic force microscopy and Raman spectroscopy confirmed that...
Most significant scientific achievement for 2025
Na₂S-assisted CVD synthesis of 2D WS₂ flakes, coordinator prof. Vera Marinova, DSc
Schematic representation of Na₂S-assisted CVD synthesis, preparation of highly crystalline 2D WS₂ flakes and investigation of their optical properties and applications in ultrathin optoelectronic devices.WS2 in mono- and few-layer forms were prepared by the reaction of S with WO3 using substrate pretreatment with sodium sulfide (Na2S) as a source of H2S, providing an efficient, cheap and environmentally friendly synthesis method. The combination of XRD, atomic force microscopy and Raman spectroscopy confirmed that the obtained samples consist of S-W-S monolayer. A rotation of the polarization plane when linearly polarized light propagates through the WS2 sample was demonstrated, which opens further possibilities for applications as ultrathin optical elements (flat optics). .
The results are published in V. Videva, B. Napoleonov, N. Minev, I. Dionisiev, P. Rafailov, D. Kovacheva, D. Dimov, V. Strijkova, S. Petrov, D. Dimitrov, S. H. Lin and V. Marinova “Na2S-mediated CVD synthesis of 2D WS2 flakes” J. Electron. Mater. 54, 5493–5500 (2025). https://doi.org/10.1007/s11664-025-11915-6.
Most significant scientific achievement for 2025
Na₂S-assisted CVD synthesis of 2D WS₂ flakes, coordinator prof. Vera Marinova, DSc
Schematic representation of Na₂S-assisted CVD synthesis, preparation of highly crystalline 2D WS₂ flakes and investigation of their optical properties and applications in ultrathin optoelectronic devices.
WS2 in mono- and few-layer forms were prepared by the reaction of S with WO3 using substrate pretreatment with sodium sulfide (Na2S) as a source of H2S, providing an efficient, cheap and environmentally friendly synthesis method. The combination of XRD, atomic force microscopy and Raman spectroscopy confirmed that the obtained samples consist of S-W-S monolayer. A rotation of the polarization plane when linearly polarized light propagates through the WS2 sample was demonstrated, which opens further possibilities for applications as ultrathin optical elements (flat optics). .The results are published in V. Videva, B. Napoleonov, N. Minev, I. Dionisiev, P. Rafailov, D. Kovacheva, D. Dimov, V. Strijkova, S. Petrov, D. Dimitrov, S. H. Lin and V. Marinova “Na2S-mediated CVD synthesis of 2D WS2 flakes” J. Electron. Mater. 54, 5493–5500 (2025). https://doi.org/10.1007/s11664-025-11915-6.
Most significant applied scientific achievement for 2025
Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning, Assoc. Prof. Violeta Madjarova, PhD, Prof. Elena Stoykova, DSc
Conventional Fourier domain Optical Coherence Tomography (FD-OCT) systems depend on resampling into a wavenumber (k) domain to extract the depth profile. This either necessitates additional hardware resources or amplifies the existing computational complexity...
Most significant applied scientific achievement for 2025
Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning
Assoc. Prof. Violeta Madjarova, PhD, Prof. Elena Stoykova, DSc
Figure:Comparison of the results from the proposed model and the ground truth images for (a) lemon and (b) vein
Table:Comparison of scores for PSNR, SSIM, CNR, β parameters for input (degraded input to SD-CNN), OCT Output (generated by the Optores OCT system), and reconstructed OCT images from the proposed framework.Method Dataset PSNR SSIM CNR β Input Lemon 7.14 0.05 5.69 0.64 Vein 12.76 0.14 4.44 0.68 OCT Output Lemon 20.40 0.26 6.73 0.78 Vein 21.20 0.22 5.21 0.88 Proposed SDCNN+FDCNN Lemon 25.74 0.54 7.98 0.91 Vein 21.98 0.43 8.71 0.93
Conventional Fourier domain Optical Coherence Tomography (FD-OCT) systems depend on resampling into a wavenumber (k) domain to extract the depth profile. This either necessitates additional hardware resources or amplifies the existing computational complexity. Moreover, the OCT images also suffer from speckle noise, due to systemic reliance on low-coherence interferometry. A streamlined and computationally efficient approach based on Deep Learning (DL) is proposed, which enables reconstructing specklereduced OCT images directly from the wavelength (λ) domain. For reconstruction, two encoder–decoder styled networks, namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN), are used sequentially. The SD-CNN exploits the highly degraded images obtained by Fourier transforming the (λ) domain fringes to reconstruct the deteriorated morphological structures along with suppression of unwanted noise. The FD-CNN leverages this output to enhance the image quality further by optimization in the Fourier domain (FD). The efficacy of the method in obtaining high-quality OCT images was quantitatively and visually demonstrated. Furthermore, the computational complexity reduction by harnessing the power of DL models was illustrated. This work lays the framework for further innovations in the realm of OCT image reconstruction.
The results were published in open access journal paper Viqar, M., Sahin, E., Stoykova, E., Madjarova, V., Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning, Sensors 2025, 25(1), 93; https://doi.org/10.3390/s25010093 and are part of the PhD thesis of Mariam Viqar, PhD, defended in January 2026.
Most significant applied scientific achievement for 2025
Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning
Assoc. Prof. Violeta Madjarova, PhD, Prof. Elena Stoykova, DSc
Assoc. Prof. Violeta Madjarova, PhD, Prof. Elena Stoykova, DSc
Figure:Comparison of the results from the proposed model and the ground truth images for (a) lemon and (b) vein
Table:Comparison of scores for PSNR, SSIM, CNR, β parameters for input (degraded input to SD-CNN), OCT Output (generated by the Optores OCT system), and reconstructed OCT images from the proposed framework.
Conventional Fourier domain Optical Coherence Tomography (FD-OCT) systems depend on resampling into a wavenumber (k) domain to extract the depth profile. This either necessitates additional hardware resources or amplifies the existing computational complexity. Moreover, the OCT images also suffer from speckle noise, due to systemic reliance on low-coherence interferometry. A streamlined and computationally efficient approach based on Deep Learning (DL) is proposed, which enables reconstructing specklereduced OCT images directly from the wavelength (λ) domain. For reconstruction, two encoder–decoder styled networks, namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN), are used sequentially. The SD-CNN exploits the highly degraded images obtained by Fourier transforming the (λ) domain fringes to reconstruct the deteriorated morphological structures along with suppression of unwanted noise. The FD-CNN leverages this output to enhance the image quality further by optimization in the Fourier domain (FD). The efficacy of the method in obtaining high-quality OCT images was quantitatively and visually demonstrated. Furthermore, the computational complexity reduction by harnessing the power of DL models was illustrated. This work lays the framework for further innovations in the realm of OCT image reconstruction.Table:Comparison of scores for PSNR, SSIM, CNR, β parameters for input (degraded input to SD-CNN), OCT Output (generated by the Optores OCT system), and reconstructed OCT images from the proposed framework.
| Method | Dataset | PSNR | SSIM | CNR | β |
|---|---|---|---|---|---|
| Input | Lemon | 7.14 | 0.05 | 5.69 | 0.64 |
| Vein | 12.76 | 0.14 | 4.44 | 0.68 | |
| OCT Output | Lemon | 20.40 | 0.26 | 6.73 | 0.78 |
| Vein | 21.20 | 0.22 | 5.21 | 0.88 | |
| Proposed SDCNN+FDCNN | Lemon | 25.74 | 0.54 | 7.98 | 0.91 |
| Vein | 21.98 | 0.43 | 8.71 | 0.93 |
The results were published in open access journal paper Viqar, M., Sahin, E., Stoykova, E., Madjarova, V., Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning, Sensors 2025, 25(1), 93; https://doi.org/10.3390/s25010093 and are part of the PhD thesis of Mariam Viqar, PhD, defended in January 2026.