Browsing by Author "Yorulmaz, M."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access A transformer-based real-time focus detection technique for wide-field interferometric microscopy(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Polat, Can; Güngör, A.; Yorulmaz, M.; Kızılelma, B.; Çukur, TolgaWide-field interferometric microscopy (WIM) has been utilized for visualization of individual biological nanoparticles with high sensitivity. However, the image quality is highly affected by the focusing of the image. Hence, focus detection has been an active research field within the scope of imaging and microscopy. To tackle this issue, we propose a novel convolution and transformer based deep learning technique to detect focus in WIM. The method is compared to other focus detecton techniques and is able to obtain higher precision with less number of parameters. Furthermore, the model achieves real-time focus detection thanks to its low inference time.Item Open Access Resolution enhancement of wide-field interferometric microscopy by coupled deep autoencoders(OSA - The Optical Society, 2018) Işıl, Ç.; Yorulmaz, M.; Solmaz, B.; Turhan, Adil Burak; Yurdakul, C.; Ünlü, S.; Özbay, Ekmel; Koç, A.Wide-field interferometric microscopy is a highly sensitive, label-free, and low-cost biosensing imaging technique capable of visualizing individual biological nanoparticles such as viral pathogens and exosomes. However, further resolution enhancement is necessary to increase detection and classification accuracy of subdiffraction-limited nanoparticles. In this study, we propose a deep-learning approach, based on coupled deep autoencoders, to improve resolution of images of L-shaped nanostructures. During training, our method utilizes microscope image patches and their corresponding manual truth image patches in order to learn the transformation between them. Following training, the designed network reconstructs denoised and resolution-enhanced image patches for unseen input.