33 citations · 63 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…