Abstract A computational imaging platform utilizing a physics-incorporated, deep-learned design of binary phase filter and a jointly optimized deconvolution neural network has been reported, achieving high-resolution, high-contrast imaging over extended depth ranges without the need for serial refocusing
Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, w...
Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its po...
A nonintrusive far-field optical microscopy resolving structures at the nanometer scale would revolu...
We use deep learning to optimize the end-to-end design of computational microscopes, jointly designi...
Exponential advancements in computational resources and algorithms have given birth to the new parad...
We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imag...
In computational imaging systems (e.g. tomographic systems, computational optics, magnetic resonance...
We present a computational imaging approach, combining a phase-coded computational camera with a cor...
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Deep learning has ...
International audienceConventional microscopy systems have limited depth of field, which often neces...
Over the past decade, deep learning has become one of the leading techniques used in the field of im...
Deep learning has revolutionised microscopy, enabling automated means for image classification, trac...
This electronic version was submitted by the student author. The certified thesis is available in th...
We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-r...
Depth of field is an important factor of imaging systems that highly affects the quality of the acqu...
Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, w...
Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its po...
A nonintrusive far-field optical microscopy resolving structures at the nanometer scale would revolu...
We use deep learning to optimize the end-to-end design of computational microscopes, jointly designi...
Exponential advancements in computational resources and algorithms have given birth to the new parad...
We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imag...
In computational imaging systems (e.g. tomographic systems, computational optics, magnetic resonance...
We present a computational imaging approach, combining a phase-coded computational camera with a cor...
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Deep learning has ...
International audienceConventional microscopy systems have limited depth of field, which often neces...
Over the past decade, deep learning has become one of the leading techniques used in the field of im...
Deep learning has revolutionised microscopy, enabling automated means for image classification, trac...
This electronic version was submitted by the student author. The certified thesis is available in th...
We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-r...
Depth of field is an important factor of imaging systems that highly affects the quality of the acqu...
Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, w...
Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its po...
A nonintrusive far-field optical microscopy resolving structures at the nanometer scale would revolu...
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