40 research outputs found
Deep, Complex, Invertible Networks for Inversion of Transmission Effects in Multimode Optical Fibres
We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of \SI{1}{\metre} and \SI{10}{\metre} long, \SI{105}{\micro\metre} diameter multimode fibre. This data is made available as the {\it Optical fibre inverse problem} Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A {\it unitary regularisation} approach for complex-weighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which fits well with the physical theory. A key benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and show how it can improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator
Deep, Complex, Invertible Networks for Inversion of Transmission Effects in Multimode Optical Fibres
We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of \SI{1}{\metre} and \SI{10}{\metre} long, \SI{105}{\micro\metre} diameter multimode fibre. This data is made available as the {\it Optical fibre inverse problem} Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A {\it unitary regularisation} approach for complex-weighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which fits well with the physical theory. A key benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and show how it can improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator
Artificial neural networks for scattered light imaging
Image formation is one of the most important aspect of our everyday life. Conventional optical Imaging (and Sensing) exploits light, reaching the detection system from a target or a scene of interest, mainly unscattered. However, there are many practical situations in which unscattered light may be undetectable, insufficient or mispresented. Nonetheless, if the considered system allows it, it could be still possible to exploit scattered light in order to extract relevant information. Problems arise from the fact that, in these cases, light propagation may undergo severe alterations, thus leading to challenging, and sometimes ill- posed, problems.
In this thesis, two main scenarios involving scattered light are studied and addressed by means of artificial neural networks. Over the last period, these powerful data-driven algorithms have been extensively employed in many scientific contexts for their ability to solve even complex problems implicitly. Precisely this characteristic is exploited, in the present work, in a non-line- of-sight scenario in order to simultaneously locate and identify people hidden behind a corner. Moreover, a complex-valued neural network algorithm is implemented and applied to the problem of transmission of images through a multimode fibre, demonstrating high-speed and high-resolution image restoration even without the need for any phase measurements. Finally, due to its formulation based on the physics of multimode fibres, a direct comparison is proposed between the same algorithm and a more standard approach
Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres
We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an SO+(2, 1) equivariant neural network. This model takes advantage of the of the azimuthal correlations known to exist in fibre speckle patterns and naturally accounts for the difference in spatial arrangement between input and speckle patterns. In addition, we use a second post-processing network to remove circular artifacts, fill gaps, and sharpen the images, which is required due to the nature of optical fibre transmission. This two stage approach allows for the inspection of the predicted images produced by the more robust physically motivated equivariant model, which could be useful in a safety-critical application, or by the output of both models, which produces high quality images. Further, this model can scale to previously unachievable resolutions of imaging with multi-mode optical fibres and is demonstrated on 256 Ă 256 pixel images. This is a result of improving the trainable parameter requirement from O(N 4) to O(m), where N is pixel size and m is number of fibre modes. Finally, this model generalises to new images, outside of the set of training data classes, better than previous models
Deep learning the high variability and randomness inside multimode fibers
Multimode fibres (MMF) are remarkable high-capacity information channels
owing to the large number of transmitting fibre modes, and have recently
attracted significant renewed interest in applications such as optical
communication, imaging, and optical trapping. At the same time, the optical
transmitting modes inside MMFs are highly sensitive to external perturbations
and environmental changes, resulting in MMF transmission channels being highly
variable and random. This largely limits the practical application of MMFs and
hinders the full exploitation of their information capacity. Despite great
research efforts made to overcome the high variability and randomness inside
MMFs, any geometric change to the MMF leads to completely different
transmission matrices, which unavoidably fails at the information recovery.
Here, we show the successful binary image transmission using deep learning
through a single MMF, which is stationary or subject to dynamic shape
variations. We found that a single convolutional neural network has excellent
generalisation capability with various MMF transmission states. This deep
neural network can be trained by multiple MMF transmission states to accurately
predict unknown information at the other end of the MMF at any of these states,
without knowing which state is present. Our results demonstrate that deep
learning is a promising solution to address the variability and randomness
challenge of MMF based information channels. This deep-learning approach is the
starting point of developing future high-capacity MMF optical systems and
devices, and is applicable to optical systems concerning other diffusing media
Robustness, scalability and interpretability of equivariant neural networks across different low-dimensional geometries
In this thesis we develop neural networks that exploit the symmetries of four different low-dimensional geometries, namely 1D grids, 2D grids, 3D continuous spaces and graphs, through the consideration of translational, rotational, cylindrical and permutation symmetries. We apply these models to applications across a range of scientific disciplines demonstrating the predictive ability, robustness, scalability, and interpretability.
We develop a neural network that exploits the translational symmetries on 1D grids to predict age and species of mosquitoes from high-dimensional mid-infrared spectra. We show that the model can learn to predict mosquito age and species with a higher accuracy than models that do not utilise any inductive bias. We also demonstrate that the model is sensitive to regions within the input spectra that are in agreement with regions identified by a domain expert. We present a transfer learning approach to overcome the challenge of working with small, real-world, wild collected data sets and demonstrate the benefit of the approach on a real-world application.
We demonstrate the benefit of rotation equivariant neural networks on the task of segmenting deforestation regions from satellite images through exploiting the rotational symmetry present on 2D grids. We develop a novel physics-informed architecture, exploiting the cylindrical symmetries of the group SO+ (2, 1), which can invert the transmission effects of multi-mode optical fibres (MMFs). We develop a new connection between a physics understanding of MMFs and group equivariant neural networks. We show that this novel architecture requires fewer training samples to learn, better generalises to out-of-distribution data sets, scales to higher-resolution images, is more interpretable, and reduces the parameter count of the model. We demonstrate the capability of the model on real-world data and provide an adaption to the model to handle real-world deviations from theory. We also show that the model can scale to higher resolution images than was previously possible.
We develop a novel architecture which provides a symmetry-preserving mapping between two different low-dimensional geometries and demonstrate its practical benefit for the application of 3D hand mesh generation from 2D images. This models exploits both the 2D rotational symmetries present in a 2D image and in a 3D hand mesh, and provides a mapping between the two data domains. We demonstrate that the model performs competitively on a range of benchmark data sets and justify the choice of inductive bias in the model.
We develop an architecture which is equivariant to a novel choice of automorphism group through the use of a sub-graph selection policy. We demonstrate the benefit of the architecture, theoretically through proving the improved expressivity and improved scalability, and experimentally on a range of widely studied benchmark graph classification tasks. We present a method of comparison between models that had not been previously considered in this area of research, demonstrating recent SOTA methods are statistically indistinguishable
Advanced photonic and electronic systems - WILGA 2017
WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers more than 350 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET by PAN and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2017 was the XL edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2017 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445
Embed Me If You Can: A Geometric Perceptron
Solving geometric tasks involving point clouds by using machine learning is a
challenging problem. Standard feed-forward neural networks combine linear or,
if the bias parameter is included, affine layers and activation functions.
Their geometric modeling is limited, which motivated the prior work introducing
the multilayer hypersphere perceptron (MLHP). Its constituent part, i.e.,
hypersphere neuron, is obtained by applying a conformal embedding of Euclidean
space. By virtue of Clifford algebra, it can be implemented as the Cartesian
dot product of inputs and weights. If the embedding is applied in a manner
consistent with the dimensionality of the input space geometry, the decision
surfaces of the model units become combinations of hyperspheres and make the
decision-making process geometrically interpretable for humans. Our extension
of the MLHP model, the multilayer geometric perceptron (MLGP), and its
respective layer units, i.e., geometric neurons, are consistent with the 3D
geometry and provide a geometric handle of the learned coefficients. In
particular, the geometric neuron activations are isometric in 3D. When
classifying the 3D Tetris shapes, we quantitatively show that our model
requires no activation function in the hidden layers other than the embedding
to outperform the vanilla multilayer perceptron. In the presence of noise in
the data, our model is also superior to the MLHP