Abstract
This paper introduces a novel generative encoder (GE) model for generative imaging and image processing with applications in compressed sensing and imaging, image compression, denoising, inpainting, deblurring, and super-resolution. The GE model consists of a pre-training phase and a solving phase. In the pre-training phase, we separately train two deep neural networks: a generative adversarial network (GAN) with a generator $\G$ that captures the data distribution of a given image set, and an auto-encoder (AE) network with an encoder $\EN$ that compresses images following the estimated distribution by GAN. In the solving phase, given a noisy image $x=\mathcal{P}(x^*)$, where $x^*$ is the target unknown image, $\mathcal{P}$ is an operator adding an addictive, or multiplicative, or convolutional noise, or equivalently given such an image $x$ in the compressed domain, i.e., given $m=\EN(x)$, we solve the optimization problem \[ z^*=\underset{z}{\mathrm{argmin}} \|\EN(\G(z))-m\|_2^2+\lambda\|z\|_2^2 \] to recover the image $x^*$ in a generative way via $\hat{x}:=\G(z^*)\approx x^*$, where $\lambda>0$ is a hyperparameter. The GE model unifies the generative capacity of GANs and the stability of AEs in an optimization framework above instead of stacking GANs and AEs into a single network or combining their loss functions into one as in existing literature. Numerical experiments show that the proposed model outperforms several state-of-the-art algorithms.
Abstract (translated)
本文介绍了一种用于生成成像和图像处理的新型生成编码器（GE）模型，该模型在压缩传感和成像、图像压缩、去噪、修复、去模糊和超分辨率等方面有着广泛的应用。GE模型包括一个预培训阶段和一个解决阶段。在预训练阶段，我们分别训练两个深层神经网络：一个生成对抗网络（g an）和一个获取给定图像集数据分布的生成器$g$和一个自动编码器（ae）网络（编码器$en$按照gan的估计分布压缩图像）。在解算阶段，给定一个噪声图像$x=mathcal p（x^*）$，其中$x^*$是目标未知图像，$mathcal p是一个添加上瘾、乘法或卷积噪声的运算符，或者在压缩域中等价地给定这样一个图像$x$，即给定$m=en（x）$，我们就解决了优化问题。
URL
https://arxiv.org/abs/1905.13300
