181 citations · 702 across the 49 of their papers we have counts for
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Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks
Stefan Bamberger, Reinhard Heckel, Felix Krahmer
We investigate to what extent it is possible to solve linear inverse problems with networks. Due to the scaling invariance arising from the linearity, an optimal reconstruct…
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…
Learning Provably Robust Estimators for Inverse Problems via Jittering
Anselm Krainovic, Mahdi Soltanolkotabi, Reinhard Heckel
Deep neural networks provide excellent performance for inverse problems such as denoising. However, neural networks can be sensitive to adversarial or worst-case perturbations. Thi…
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…
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data
Youssef Mansour, Reinhard Heckel
Recently, self-supervised neural networks have shown excellent image denoising performance. However, current dataset free methods are either computationally expensive, require a no…