Fast Unsupervised Tensor Restoration via Low-rank Deconvolution
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Abstract (arXiv)
Low-rank Deconvolution (LRD) has appeared as a new multi-dimensional representation model that enjoys important efficiency and flexibility properties. In this work we ask ourselves if this analytical model can compete against Deep Learning (DL) frameworks like Deep Image Prior (DIP) or Blind-Spot Networks (BSN) and other classical methods in the task of signal restoration. More specifically, we propose to extend LRD with differential regularization. This approach allows us to easily incorporate Total Variation (TV) and integral priors to the formulation leading to considerable performance tested on signal restoration tasks such image denoising and video enhancement, and at the same time benefiting from its small computational cost.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 15 Jun 2024.
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10 of 23 references.
References · 10
- 2017Deep Image Priorcited by 1,097
- 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 407
- 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 92
- 2018Image Restoration using Total Variation Regularized Deep Image Priorcited by 65
- 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
- 2023Random Sub-Samples Generation for Self-Supervised Real Image Denoisingcited by 31
- 2021Noise2Fast: Fast Self-Supervised Single Image Blind Denoisingcited by 28
- 2017Convolutional Dictionary Learning: A Comparative Review and New Algorithmscited by 23
- 2017Convolutional Dictionary Learning via Local Processingcited by 19
- 2023Multi-dimensional Signal Recovery using Low-rank Deconvolutioncited by 1
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