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Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems.
Theoretical foundations of transform coding
Goyal, V. K · 2001
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Pattern Recognition and Machine Learning
Bishop, C. M · 2006
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Structured uncertainty prediction networks
Dorta, G., Vicente, S., Agapito, L., Campbell, N. D., and Simpson, I · 2018
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Rezende, D. J. and Viola, F · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Nonlinear transform coding
Ballé, J., Chou, P. A., Minnen, D., Singh, S., Johnston, N., Agustsson, E., Hwang, S. J., and Toderici, G · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Deep unfolding network for image super-resolution
Zhang, K., Gool, L. V., and Timofte, R · 2020
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ILVR: Conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
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Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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Estimating high order gradients of the data distribution by denoising
Meng, C., Song, Y., Li, W., and Ermon, S · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Maximum likelihood training for score-based diffusion odes by high order denoising score matching
Lu, C., Zheng, K., Bao, F., Chen, J., Li, C., and Zhu, J · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
SUD 2 : Supervision by denoising diffusion models for image reconstruction
Chan, M. A., Young, S. I., and Metzler, C. A · 2023
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Score-based diffusion models as principled priors for inverse imaging
Feng, B. T., Smith, J., Rubinstein, M., Chang, H., Bouman, K. L., and Freeman, W. T · 2023
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Refusion: Enabling large-size realistic image restoration with latent-space diffusion models
Luo, Z., Gustafsson, F. K., Zhao, Z., Sjölund, J., and Schön, T. B · 2023
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Uncertainty quantification via neural posterior principal components
Nehme, E., Yair, O., and Michaeli, T · 2023
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Training-free linear image inversion via flows
Pokle, A., Muckley, M. J., Chen, R. T., and Karrer, B · 2023
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Image super-resolution via iterative refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M · 2022
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Solving inverse problems in medical imaging with score-based generative models
Song, Y., Shen, L., Xing, L., and Ermon, S · 2022
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Deblurring via stochastic refinement
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., and Milanfar, P · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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Transformer-based transform coding
Zhu, Y., Yang, Y., and Cohen, T · 2022
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Tweedie moment projected diffusions for inverse problems
Boys, B., Girolami, M., Pidstrigach, J., Reich, S., Mosca, A., and Akyildiz, O. D · 2023
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Ravula, S., Levac, B., Jalal, A., Tamir, J. I., and Dimakis, A. G · 2023
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Beyond first-order Tweedie: Solving inverse problems using latent diffusion
Rout, L., Chen, Y., Kumar, A., Caramanis, C., Shakkottai, S., and Chu, W.-S · 2023
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Pseudoinverse-guided diffusion models for inverse problems
Song, J., Vahdat, A., Mardani, M., and Kautz, J · 2023
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Zero-shot image restoration using denoising diffusion null-space model
Wang, Y., Yu, J., and Zhang, J · 2023
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Denoising diffusion models for plug-and-play image restoration
Zhu, Y., Zhang, K., Liang, J., Cao, J., Wen, B., Timofte, R., and Van Gool, L · 2023
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Frequency-aware transformer for learned image compression
Li, H., Li, S., Dai, W., Li, C., Zou, J., and Xiong, H · 2024
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A variational perspective on solving inverse problems with diffusion models
Mardani, M., Song, J., Kautz, J., and Vahdat, A · 2024
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