4.3. Image Processing Applications 在高保真图像重建的情况下,我们的多码反演方法与之前一样,用预先训练过的GANs简化了许多图像处理任务。在本节中,我们将提出的mGANprior应用于各种实际应用程序,以演示其有效性,包括图像着色、图像超分辨率、图像修补和去噪,以及语义操作和样式混合。对于每个应用程序,GAN模型都是固定...
因为现在 GAN 的生成效果已经非常不错,但是他们的输入都是随机噪声,而我们的需求肯定是对一张已有的图片进行操作,所以 GAN 逆推这个主题是很关键的,是 GAN 编辑和生成的基础。GAN 的逆推就是生成器的反过程,生成器是把一个向量输入,输出一张图片,逆推的效果是输入一张图片,输出一个他在 GAN 空间中的向量 a ...
论文:Image Processing Using Multi-Code GAN Prior, CVPR2020代码:github.com/genforce/mga 这是来自香港中文大学周博磊老师l团队的工作。 尽管生成式对抗网络(GANs)在图像合成方面取得了成功,StyleGAN和BigGAN能够合成高质量的图像。这些方法能够从大量观测数据中捕捉多种层次的语义信息。但当前研究还没有把这些训练好的...
In this work, we propose a new inversion approach to applying well-trained GANs as effective prior to a variety of image processing tasks, such as image colorization, super-resolution, image inpainting, and semantic manipulation. Image Processing Using Multi-Code GAN Prior ...
Image Processing Using Multi-Code GAN Prior Paper:https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.html Code:https://github.com/genforce/mganprior EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-...
title = {Image Processing Using Multi-Code GAN Prior}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020} } 代码github https://github.com/genforce/mganprior 摘要:
We propose a two-stage framework, the GAN of the target domain is pretrained to obtain the prior information in the first stage, the pretrained GAN for the target domain is embedded as the decoder of the translation network and the translation network is trained with the guidance of the ...
Paper: High-Resolution Image Inpainting using Multi-Scale Neural Patch Systhesis, GitHub: leehomyc/High-Res-Neural-Inpainting 两篇论文的网络框架基本一样: 主要思路都是结合Encoder-Decoder 网络结构和 GAN (Generative Adversarial Networks),Encoder-Decoder 阶段用于学习图像特征和生成图像待修补区域对应的预测图...
Johari and Behroozi (2020a) also found that the training process of GANs can be stabilized by using the TV loss function. 2.3.6 Adversarial loss In recent years, GANs have been widely used in various image processing tasks because of its powerful generative capability, such as image ...
由此,文中作者提出一种叫做多码GAN先验(multi-code GAN prior,简称mGANprior)的方法,将已经训练完好的GAN模型作为一种有效先验,用于多种图像处理任务中。具体而言,作者在生成器(generator)的中间层用多种潜码(multiple latent code)来生成多种特征图(feature map),并通过自适应通道重要性机制(adaptive channel ...