Generative Flows as a General Purpose Solution for Inverse Problems
- URL: http://arxiv.org/abs/2110.13285v1
- Date: Mon, 25 Oct 2021 21:56:44 GMT
- Title: Generative Flows as a General Purpose Solution for Inverse Problems
- Authors: Jos\'e A. Ch\'avez
- Abstract summary: We propose a regularization term to directly produce high likelihood reconstructions.
We evaluate our method in image denoising, image deblurring, image inpainting, and image colorization.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Due to the success of generative flows to model data distributions, they have
been explored in inverse problems. Given a pre-trained generative flow,
previous work proposed to minimize the 2-norm of the latent variables as a
regularization term in the main objective. The intuition behind it was to
ensure high likelihood latent variables, however this does not ensure the
generation of realistic samples as we show in our experiments. We therefore
propose a regularization term to directly produce high likelihood
reconstructions. Our hypothesis is that our method could make generative flows
a general-purpose solver for inverse problems. We evaluate our method in image
denoising, image deblurring, image inpainting, and image colorization. We
observe a compelling improvement of our method over prior works in the PSNR and
SSIM metrics.
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