Searched for: subject%3A%22denoising%22
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Mullaj, Dajt (author)
Deep convolutional neural networks (CNNs) have achieved current state-of-the-art in image denoising, but require large datasets for training. Their performance remains limited on smaller real-noise datasets. In this paper, we investigate robust deep learning denoising using transfer learning. We explore the impact of dataset sizes, CNN parameter...
master thesis 2023
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Markhorst, Thomas (author)
In this paper, we combine image denoising and classification, aiming to enhance human perception of noisy images captured by edge devices, like security cameras. Since edge devices have little computational power, we also optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural...
master thesis 2023
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Huizer, Rick (author)
Automated imaging systems, critical in domains like medical imaging, autonomous driving, and security, experience noise from camera sensors and electronic circuits in bad or dark lighting conditions. This impacts downstream tasks, including object detection. However, an analysis of strategies combining denoising and object detection is lacking....
master thesis 2023
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Smolin, Nikita (author)
This study aims to investigate the impact of various denoising algorithms on the quality of visual stimulus reconstructions based on functional magnetic resonance imaging (fMRI) data. While fMRI provides a valuable, noninvasive method for assessing brain activity, the reliability of this data can be impaired by multiple noise types, including...
bachelor thesis 2023
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Shan, X. (author), van Gijzen, M.B. (author)
This paper studies time-domain parallelisation using Parareal to speed up the computations of anisotropic diffusion filtering. We consider both explicit and implicit Euler based method for the propagation in time for Parareal. The Preconditioned Conjugate Gradient (PCG) method is used to solve the systems that arise in the implicit based...
conference paper 2023
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Mohammadkarimi, M. (author), Ardakani, Masoud (author)
We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the...
journal article 2023
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Wong, Jing Jing (author)
This research consists of two applications of image processing, namely, image compression and image denoising. Image compression aims to reduce the size of an image without losing too many features. This is often used to store a large number of images such as fingerprints. Denoising is a technique for removing noise from an image while...
bachelor thesis 2022
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Ligthart, Liam (author)
More insight has been gathered on the performance of the SPLITTER (Stationary spectrum Plus Low-rank Iterative TransmiTtance EstimatoR) algorithm as developed by Brackenhoff for denoising data gathered from observations of high-redshift galaxies. By using matrix decomposition to split the gathered data into a low-rank atmosphere matrix and a...
bachelor thesis 2022
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Koers, Pallas (author)
Adaptive Deep Brain Stimulation (aDBS) offers the potential for personalized stimulation strategies for patients with Parkinson's Disease (PD). The closed loop characteristic of this system requires the incorporation of PD relevant biomarkers that determine the patient's need. In order to obtain high quality LFP (Local Field Potential) input...
master thesis 2022
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Shan, X. (author), van Gijzen, M.B. (author)
We study efficient implicit methods to denoise low-field MR images using a nonlinear diffusion operator as a regularizer. This problem can be formulated as solving a nonlinear reaction–diffusion equation. After discretization, a lagged-diffusion approach is used which requires a linear system solve in every nonlinear iteration. The choice of...
journal article 2022
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Lourenço Baptista, M. (author), Henriques, Elsa M.P. (author)
The performance of prognostics is closely related to the quality of condition monitoring signals (e.g., temperature, pressure, or vibration signals), which reveal the degradation of the system of interest. However, typical condition monitoring signals include noise and outliers. Disentangling noise from these signals is essential to obtain...
journal article 2022
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Veldhuis, Erik (author)
This research proposes a new differentiator for estimating higher order derivatives of an input signal. The main reason why higher order derivatives are necessary is that Active Inference makes use of generalized coordinates. This means that it keeps internally track of higher order temporal derivatives of states, inputs and measurements. The...
master thesis 2021
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Bansal, Ishita Rai (author)
Resting-state functional magnetic resonance imaging (rs-fMRI) is one of the promising non-invasive technology that helps in the detection of neurodegenerative and neurological disorders, localisation of the different areas of the brain and understanding the connectivity between them. It involves the acquisition of time series of MR images while...
master thesis 2021
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Zhang, Rongkai (author), Zhu, Jiang (author), Zha, Zhiyuan (author), Dauwels, J.H.G. (author), Wen, Bihan (author)
State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions. Though deep reinforcement learning can generate effective policy networks for operator selection or architecture...
conference paper 2021
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Ahishakiye, Emmanuel (author), van Gijzen, M.B. (author), Shan, X. (author), Tumwiine, Julius (author), Obungoloch, Johnes (author)
Currently, many children with hydrocephalus in East Africa and other resource-constrained countries do not have access to Magnetic Resonance Imaging (MRI) scanners, the preferred imaging tool during the disease administration and treatment. Conventional MRI scanners are costly to buy and manage, which limits their utilization in low-income...
conference paper 2021
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Bakos, Máté (author)
In the field of biomedical imaging, images often report a combination of biologically induced variation, usually the goal of the imaging process (e.g. outlining an anatomical region or disease pattern), and non-biological variation, such as instrument or acquisition method-induced noise patterns. <br/>Since some medical decisions are made based...
master thesis 2020
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Wu, L. (author), Picek, S. (author)
In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that deep learning attacks will always succeed. Various countermeasures make attacks significantly more complex, and such countermeasures can be further...
journal article 2020
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Yang, M. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
While regularization on graphs has been successful for signal reconstruction, strategies for controlling the bias-variance trade-off of such methods have not been completely explored. In this work, we put forth a node varying regularizer for graph signal reconstruction and develop a minmax approach to design the vector of regularization...
conference paper 2020
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Ahishakiye, Emmanuel (author), van Gijzen, M.B. (author), Tumwiine, Julius (author), Obungoloch, Johnes (author)
Objective: Image denoising has been considered as a separate procedure from image reconstruction which could otherwise be combined with acquisition and reconstruction. This paper discusses a joint image reconstruction and denoising algorithm in low-field MRI using a dictionary learning approach. Method: Our proposed algorithm uses a two-level...
conference paper 2020
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Claus, Michele (author)
We propose a novel Convolutional Neural Network (CNN) for Video Denoising called VidCNN, which is capable to denoise videos without prior knowledge on the noise distribution (Blind). VidCNN is a flexible model, since it tackles multiple noise types, artificial and real. The CNN architecture uses a combination of spatial and temporal filtering,...
master thesis 2018
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