We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements. This formulation allows us to use noise models with arbitrary dependencies as well as non-linear forward operators. We empirically validate the efficacy of our method on various inverse problems, including compressed sensing with quantized measurements and denoising with highly structured noise patterns. We also present initial theoretical recovery guarantees for solving inverse problems with a flow prior.
翻译:我们研究的是先前正常流流的反向问题。 我们的配方将解决方案视为对测量结果所必须的图像的事后最大估计。 这种配方允许我们使用任意依赖和非线性前方操作员的噪音模型。 我们从经验上验证了我们方法在各种反向问题上的有效性,包括用量化测量的压缩感测和高度结构化的噪音模式的分泌。 我们还提出了初步的理论回收保证,以解决先前流动的反向问题。