This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks. We employ two separate deep generative models - one trained to produce sharp images while the other trained to generate blur kernels from lower dimensional parameters. To deblur, we propose an alternating gradient descent scheme operating in the latent lower-dimensional space of each of the pretrained generative models. Our experiments show excellent deblurring results even under large blurs and heavy noise. To improve the performance on rich image datasets not well learned by the generative networks, we present a modification of the proposed scheme that governs the deblurring process under both generative and classical priors.
翻译:本文提出了一种新颖的办法,用深厚的基因网络来规范失明的盲人图像变异(盲人图像变异)问题。我们采用了两种不同的深层基因模型:一种是用来制作锐利图像的训练,另一种是用来从低维参数中产生模糊的内核的训练。对于Deblur,我们建议采用一种交替的梯度下降计划,在每一个未经训练的基因变异模型潜伏的低维空间内运作。我们的实验显示即使在大模糊和重噪音下也会产生极好的变异结果。为了改进基因变异网络没有很好学到的丰富图像数据集的性能,我们提出了在基因变异和古典古典前科下管理脱泡过程的拟议办法的修改。