Browsing by Subject "Focus detection"

Now showing 1 - 3 of 3

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, Tolga
Wide-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.
Embargo
High-precision laser focus positioning of rough surfaces by deep learning
(Elsevier Ltd, 2023-05-18) Polat, Can; Yapici, Gizem Nuran; Elahi, Sepehr; Elahi, Parviz
This work presents a precise positioning detection based on a convolutional neural network (CNN) to control the laser focus in laser material processing systems. The images of the diffraction patterns measured at different positions of the laser focus concerning the workpiece are classified in the range of the Rayleigh length of the focusing lens with an increment of about 7% of it. The experiment was carried out on different materials with different levels of surface roughness, such as copper, silicon, and steel, and over 99% accuracy in the positioning detection was achieved. Considering surface roughness and camera noise, a theoretical model is established, and the effects of these parameters on the accuracy of focus detection are also presented. The proposed method exhibits a noise-robust focus detection system and the potential for many precise positioning detection systems in industry and biology. © 2023 Elsevier Ltd.
Open Access
Noise robust focal distance detection in laser material processing using CNNs and Gaussian processes
(S P I E - International Society for Optical Engineering, 2022-05-17) Elahi, Sepehr; Polat, Can; Safarzadeh, Omid; Elahi, Parviz
In this work, we investigate the effects of noise on real-time focal distance control for laser material processing by generating the images of a sample at different focal lengths using Fourier optics and then designing, training, and testing a deep learning model in order to detect the focal distances from the simulated images with varying standard deviations of added noise. We simulate both input noise, such as noise due to surface roughness, and output noise, such as detection camera noise, by adding zero-mean Gaussian noise to the source wave and the simulated image, respectively, for different focal distances. We then train a convolutional neural network combined with a Gaussian process classifier to predict focus distances of noisy images together with confidence ratings for the predictions.