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pro vyhledávání: '"Adrai, Theo"'
We propose an image restoration algorithm that can control the perceptual quality and/or the mean square error (MSE) of any pre-trained model, trading one over the other at test time. Our algorithm is few-shot: Given about a dozen images restored by
Externí odkaz:
http://arxiv.org/abs/2306.02342
JPEG is arguably the most popular image coding format, achieving high compression ratios via lossy quantization that may create visual artifacts degradation. Numerous attempts to remove these artifacts were conceived over the years, and common to mos
Externí odkaz:
http://arxiv.org/abs/2211.11827
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:26474-26494, 2023
Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use
Externí odkaz:
http://arxiv.org/abs/2211.08944
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 2021, pp. 1805-1813
The vast work in Deep Learning (DL) has led to a leap in image denoising research. Most DL solutions for this task have chosen to put their efforts on the denoiser's architecture while maximizing distortion performance. However, distortion driven sol
Externí odkaz:
http://arxiv.org/abs/2103.04192