181 citations · 702 across the 49 of their papers we have counts for
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K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets
Frederic Wang, Han Qi, Alfredo De Goyeneche +3
Although deep learning (DL) methods are powerful for solving inverse problems, their reliance on high-quality training data is a major hurdle. This is significant in high-dimension…
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
Tobit Klug, Dogukan Atik, Reinhard Heckel
Supervised training of deep neural networks on pairs of clean image and noisy measurement achieves state-of-the-art performance for many image reconstruction tasks, but such traini…
Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction
Johannes F. Kunz, Stefan Ruschke, Reinhard Heckel
Cardiac magnetic resonance imaging (MRI) requires reconstructing a real-time video of a beating heart from continuous highly under-sampled measurements. This task is challenging si…
Scaling Laws For Deep Learning Based Image Reconstruction
Tobit Klug, Reinhard Heckel
Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems. Current methods are onl…
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel
Deep learning based image reconstruction methods outperform traditional methods. However, neural networks suffer from a performance drop when applied to images from a different dis…
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi
Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an im…
Measuring Robustness in Deep Learning Based Compressive Sensing
Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel
Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imagi…
Accelerated MRI with Un-trained Neural Networks
Mohammad Zalbagi Darestani, Reinhard Heckel
Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems. Typically, CNNs are trained on large amounts of training images. Recently, however, un-…
Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network
Emrah Bostan, Reinhard Heckel, Michael Chen +2
Deep neural networks have emerged as effective tools for computational imaging including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity,…