Tootfinder

DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
Yuliang Zhu, Jing Cheng, Qi Xie, Zhuo-Xu Cui, Qingyong Zhu, Yuanyuan Liu, Xin Liu, Jianfeng Ren, Chengbo Wang, Dong Liang
https://arxiv.org/abs/2506.10309

DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
Dynamic Magnetic Resonance Imaging (MRI) exhibits transformation symmetries, including spatial rotation symmetry within individual frames and temporal symmetry along the time dimension. Explicit incorporation of these symmetry priors in the reconstruction model can significantly improve image quality, especially under aggressive undersampling scenarios. Recently, Equivariant convolutional neural network (ECNN) has shown great promise in exploiting spatial symmetry priors. However, existing ECNN…

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new/
[1/1]:
Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
Chang Sun, Zhiqiang Que, Vladimir Loncar, Wayne Luk, Maria Spiropulu
https://arxiv.org/abs/2507.04535

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
Neural networks with a latency requirement on the order of microseconds, like the ones used at the CERN Large Hadron Collider, are typically deployed on FPGAs fully unrolled and pipelined. A bottleneck for the deployment of such neural networks is area utilization, which is directly related to the required constant matrix-vector multiplication (CMVM) operations. In this work, we propose an efficient algorithm for implementing CMVM operations with distributed arithmetic (DA) on FPGAs that simult…

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new/
[1/1]:
Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers

Low-Latency Software Polar Encoders and Decoders for Short Blocklengths
Mathieu Leonardon (IMT Atlantique - MEE, Lab\_STICC\_BRAIn), Mohammed El Houcine Ayoubi (IMT Atlantique), Adrien Cassagne (ALSOC), Romain Tajan (Bordeaux INP, UB), Camille Leroux (Bordeaux INP, UB)
https://arxiv.org/abs/2507.04734

Low-Latency Software Polar Encoders and Decoders for Short Blocklengths
This paper presents our low-latency Polar code encoders and decoders developed for the 2025 International Symposium on Topics in Coding (ISTC 2025) contest, which challenges participants to implement the fastest possible channel code encoders and decoders in terms of average and maximum latency on a CPU target. Our solution is based on Polar codes with an Adaptive Successive Cancellation List (ASCL) decoder. We introduce a novel ASCL unrolled decoder generator. We conduct an extensive explorati…

Unrolling Nonconvex Graph Total Variation for Image Denoising
Songlin Wei, Gene Cheung, Fei Chen, Ivan Selesnick
https://arxiv.org/abs/2506.02381 https://

Unrolling Nonconvex Graph Total Variation for Image Denoising
Conventional model-based image denoising optimizations employ convex regularization terms, such as total variation (TV) that convexifies the $\ell_0$-norm to promote sparse signal representation. Instead, we propose a new non-convex total variation term in a graph setting (NC-GTV), such that when combined with an $\ell_2$-norm fidelity term for denoising, leads to a convex objective with no extraneous local minima. We define NC-GTV using a new graph variant of the Huber function, interpretable …

Bridging Classical and Learning-based Iterative Registration through Deep Equilibrium Models
Yi Zhang, Yidong Zhao, Qian Tao
https://arxiv.org/abs/2507.00582

Bridging Classical and Learning-based Iterative Registration through Deep Equilibrium Models
Deformable medical image registration is traditionally formulated as an optimization problem. While classical methods solve this problem iteratively, recent learning-based approaches use recurrent neural networks (RNNs) to mimic this process by unrolling the prediction of deformation fields in a fixed number of steps. However, classical methods typically converge after sufficient iterations, but learning-based unrolling methods lack a theoretical convergence guarantee and show instability empir…