[HTML][HTML] Deep learning in optical metrology: a review
With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
Artificial intelligence-enabled quantitative phase imaging methods for life sciences
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and
label-free investigation of the physiology and pathology of biological systems. This review …
label-free investigation of the physiology and pathology of biological systems. This review …
Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
Methods for image denoising using convolutional neural network: a review
AE Ilesanmi, TO Ilesanmi - Complex & Intelligent Systems, 2021 - Springer
Image denoising faces significant challenges, arising from the sources of noise. Specifically,
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …
Phase imaging with an untrained neural network
Most of the neural networks proposed so far for computational imaging (CI) in optics employ
a supervised training strategy, and thus need a large training set to optimize their weights …
a supervised training strategy, and thus need a large training set to optimize their weights …
Imaging in complex media
Imaging can take many forms—from optical microscopes and telescopes through
ultrasonography to X-ray tomography. However, regardless of the imaging modality, the …
ultrasonography to X-ray tomography. However, regardless of the imaging modality, the …
On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Roadmap on wavefront sha** and deep imaging in complex media
The last decade has seen the development of a wide set of tools, such as wavefront
sha**, computational or fundamental methods, that allow us to understand and control …
sha**, computational or fundamental methods, that allow us to understand and control …
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Imaging through scattering is an important yet challenging problem. Tremendous progress
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …
Deep learning in holography and coherent imaging
Recent advances in deep learning have given rise to a new paradigm of holographic image
reconstruction and phase recovery techniques with real-time performance. Through data …
reconstruction and phase recovery techniques with real-time performance. Through data …