391 research outputs found
Advanced Techniques for Ground Penetrating Radar Imaging
Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives
Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework
International audienceHyperspectral images are corrupted by a combination of Gaussian-impulse noise. On one hand, the traditional approach of handling the denoising problem using maximum a posteriori criterion is often restricted by the time-consuming iterative optimization process and design of hand-crafted priors to obtain an optimal result. On the other hand, the discriminative learning-based approaches offer fast inference speed over a trained model; but are highly sensitive to the noise level used for training. A discriminative model trained with a loss function which does not accord with the Bayesian degradation process often leads to sub-optimal results. In this paper, we design the training paradigm emphasizing the role of loss functions; similar to as observed in model-based optimization methods. As a result; loss functions derived in Bayesian setting and employed in neural network training boosts the denoising performance. Extensive analysis and experimental results on synthetically corrupted and real hyperspectral dataset suggest the potential applicability of the proposed technique under a wide range of homogeneous and heterogeneous noisy settings. INDEX TERMS Bayesian estimation, discriminative learning, Gaussian-impulse noise, hyperspectral imaging, residual network
Sleep Stage Classification: A Deep Learning Approach
Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed.
In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers.
For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity
Data-Driven Image Restoration
Every day many images are taken by digital cameras, and people
are demanding visually accurate and pleasing result. Noise and
blur degrade images captured by modern cameras, and high-level
vision tasks (such as segmentation, recognition, and tracking)
require high-quality images. Therefore, image restoration
specifically, image
deblurring and image denoising is a critical preprocessing step.
A fundamental problem in image deblurring is to recover reliably
distinct spatial frequencies that have been suppressed by the
blur kernel. Existing image deblurring techniques often rely on
generic image priors that only help recover part of the frequency
spectrum, such as the frequencies near the high-end. To this end,
we pose the following specific questions: (i) Does class-specific
information offer an advantage over existing generic priors for
image quality restoration? (ii) If a class-specific prior exists,
how should it be encoded into a deblurring framework to recover
attenuated image frequencies? Throughout this work, we devise a
class-specific prior based on the band-pass filter responses and
incorporate it into a deblurring strategy. Specifically, we show
that the subspace of band-pass filtered images and their
intensity distributions serve as useful priors for recovering
image frequencies.
Next, we present a novel image denoising algorithm that uses
external, category specific image database. In contrast to
existing noisy image restoration algorithms, our method selects
clean image “support patches” similar to the noisy patch from
an external database. We employ a content adaptive distribution
model for each patch where we derive the parameters of the
distribution from the support patches. Our objective function
composed of a Gaussian fidelity term that imposes category
specific information, and a low-rank term that encourages the
similarity between the noisy and the support patches in a robust
manner.
Finally, we propose to learn a fully-convolutional network model
that consists of a Chain of Identity Mapping Modules (CIMM) for
image denoising. The CIMM structure possesses two distinctive
features that are important for the noise removal task. Firstly,
each residual unit employs identity mappings as the skip
connections and receives pre-activated input to preserve the
gradient magnitude propagated in both the forward and backward
directions. Secondly, by utilizing dilated kernels for the
convolution layers in the residual branch, each neuron in the
last convolution layer of each module can observe the full
receptive field of the first layer
Realtime image noise reduction FPGA implementation with edge detection
The purpose of this dissertation was to develop and implement, in a Field
Programmable Gate Array (FPGA), a noise reduction algorithm for real-time
sensor acquired images. A Moving Average filter was chosen due to its
fulfillment of a low demanding computational expenditure nature, speed, good
precision and low to medium hardware resources utilization. The technique is
simple to implement, however, if all pixels are indiscriminately filtered, the result
will be a blurry image which is undesirable.
Since human eye is more sensitive to contrasts, a technique was
introduced to preserve sharp contour transitions which, in the author’s opinion,
is the dissertation contribution. Synthetic and real images were tested.
Synthetic, composed both with sharp and soft tone transitions, were generated
with a developed algorithm, while real images were captured with an 8-kbit
(8192 shades) high resolution sensor scaled up to 10 Ă— 103 shades.
A least-squares polynomial data smoothing filter, Savitzky-Golay, was
used as comparison. It can be adjusted using 3 degrees of freedom ─ the
window frame length which varies the filtering relation size between pixels’
neighborhood, the derivative order, which varies the curviness and the
polynomial coefficients which change the adaptability of the curve. Moving
Average filter only permits one degree of freedom, the window frame length.
Tests revealed promising results with 2 and 4â„Ž polynomial orders. Higher
qualitative results were achieved with Savitzky-Golay’s better signal
characteristics preservation, especially at high frequencies.
FPGA algorithms were implemented in 64-bit integer registers serving
two purposes: increase precision, hence, reducing the error comparatively as if
it were done in floating-point registers; accommodate the registers’ growing
cumulative multiplications. Results were then compared with MATLAB’s double
precision 64-bit floating-point computations to verify the error difference
between both. Used comparison parameters were Mean Squared Error, Signalto-Noise Ratio and Similarity coefficient.O objetivo desta dissertação foi desenvolver e implementar, em FPGA,
um algoritmo de redução de ruĂdo para imagens adquiridas em tempo real.
Optou-se por um filtro de MĂ©dia Deslizante por nĂŁo exigir uma elevada
complexidade computacional, ser rápido, ter boa precisão e requerer moderada
utilização de recursos. A técnica é simples, mas se abordada como filtragem
monotónica, o resultado é uma indesejável imagem desfocada.
Dado o olho humano ser mais sensĂvel ao contraste, introduziu-se uma
técnica para preservar os contornos que, na opinião do autor, é a sua principal
contribuição. Utilizaram-se imagens sintéticas e reais nos testes. As sintéticas,
compostas por fortes e suaves contrastes foram geradas por um algoritmo
desenvolvido. As reais foram capturadas com um sensor de alta resolução de
8-kbit (8192 tons) e escalonadas a 10 Ă— 103 tons.
Um filtro com suavização polinomial de mĂnimos quadrados, SavitzkyGolay, foi usado como comparação. Possui 3 graus de liberdade: o tamanho da
janela, que varia o tamanho da relação de filtragem entre os pixels vizinhos; a
ordem da derivada, que varia a curvatura do filtro e os coeficientes polinomiais,
que variam a adaptabilidade da curva aos pontos a suavizar. O filtro de MĂ©dia
Deslizante é apenas ajustável no tamanho da janela. Os testes revelaram-se
promissores nas 2ÂŞ e 4ÂŞ ordens polinomiais. Obtiveram-se resultados
qualitativos com o filtro Savitzky-Golay que detĂ©m melhores caracterĂsticas na
preservação do sinal, especialmente em altas frequências.
Os algoritmos em FPGA foram implementados em registos de vĂrgula
fixa de 64-bits, servindo dois propĂłsitos: aumentar a precisĂŁo, reduzindo o erro
comparativamente ao terem sido em vĂrgula flutuante; acomodar o efeito
cumulativo das multiplicações. Os resultados foram comparados com os
cálculos de 64-bits obtidos pelo MATLAB para verificar a diferença de erro
entre ambos. Os parâmetros de medida foram MSE, SNR e coeficiente de
Semelhança
Plenoptic Signal Processing for Robust Vision in Field Robotics
This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications
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