Compressed Sensing with Invertible Generative Models and Dependent Noise
We study image inverse problems with invertible generative priors, specifically normalizing flow models. Our formulation views the solution as the maximum a posteriori (MAP) estimate of the image given the measurements. Our general formulation allows for any differentiable noise model with long-range dependencies as well as non-linear differentiable forward operators. We establish theoretical recovery guarantees for denoising and compressed sensing under our framework. We also empirically validate our method on various inverse problems including 1-bit compressed sensing and denoising with highly structured noise patterns.
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