33 citations · 63 across the 8 of their papers we have counts for
10 papers
Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features
Chenqian Le, Ruisi Li, Beatrice Fumagalli +6
We test whether Speech Articulatory Coding (SPARC) features can linearly predict surface electromyography (sEMG) envelopes across aloud, mimed, and subvocal speech in twenty-four s…
Noisy MRI Reconstruction via MAP Estimation with an Implicit Deep-Denoiser Prior
Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang +1
Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstruc…
Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping
Nikasadat Emami, Amirhossein Khalilian-Gourtani, Jianghao Qian +4
Identifying cortical regions critical for speech is essential for safe brain surgery in or near language areas. While Electrical Stimulation Mapping (ESM) remains the clinical gold…
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction
Nikola Janjusevic, Amirhoussein Khalilian-Gourtani, Yao Wang +1
Magnetic resonance imaging (MRI) reconstruction has largely been dominated by deep neural networks (DNN); however, many state-of-the-art architectures use black-box structures, whi…
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant Attention
Nikola Janjusevic, Amirhossein Khalilian-Gourtani, Adeen Flinker +2
Nonlocal self-similarity within images has become an increasingly popular prior in deep-learning models. Despite their successful image restoration performance, such models remain…
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
Nikola Janjušević, Amirhossein Khalilian-Gourtani, Adeen Flinker +1
Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models. Despite their successful image restoration performance, such models…
Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Prior
Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang
Image processing neural networks, natural and artificial, have a long history with orientation-selectivity, often described mathematically as Gabor filters. Gabor-like filters have…
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing
Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang
Deep learning based methods hold state-of-the-art results in low-level image processing tasks, but remain difficult to interpret due to their black-box construction. Unrolled optim…
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning
Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang
Deep learning based methods hold state-of-the-art results in image denoising, but remain difficult to interpret due to their construction from poorly understood building blocks suc…
Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection
Amirhossein Khalilian-Gourtani, Shervin Minaee, Yao Wang
Foreground detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance system. Robust Principal Component Analysis (RPCA)…