Image Inpainting is a task of reconstructing missing regions in an image. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, e.g. object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering...
ON SEN12MS-CR 2020 SOTA! MAE 0.029 RMSE 0.036 PSNR 28.7 SAM 8.15 SSIM 0.875 ReLU 2020-07 TensorFlow CPU 查看项目 global-local-free-form-inpainting - ON Places2 2020 SOTA! L1-loss 2.23 free-form mask l2 err 0.36 40-50% Mask PSNR 28.89 SSIM 92.21 - 2020-06 PyTorch CPU 查看项目 Residu...
采用编码解码器结构提取掩摸图像的特征,并利用SPADE[4]生成器来合并输入图像的调制样式,并逐渐将纹理特征向上采样至目标分辨率。 2)损失函数: 重构损失: 对抗训练损失(纹理生成器G_t,以及判别器D): 感知损失: 实验结果 训练数据:CelebA-HQ、Places2、Paris StreetView 生成掩摸采用文献[5]的方法 度量标准: Frech...
Image inpainting using OpenCV & DeepFillv2 (places2, celebahq) on Streamlit The project was created to demonstrate tools for restoring voids in an image (not generating) Created for my friend. Example By default, the app prompts you to upload your image or use your webcam. Next you create...
作者在三个公共数据集上对HAN进行了评估,包括Paris,CelebA-HQ和Places2,实验中图像分辨率调整至256×256。在CelebA-HQ中选择前2000张图像作为测试集,其余的作为训练集。对Paris和Places2使用原始数据划分情况。 HAN基于Pytorch平台实现,训练过程中batch-size设置为6,使用AdamW优化器,其中β_1 = 0.5,β_2 = 0.9。
解析:Encoder的输入是带有mask的图片,解码器输出的是一张inpainting的图片。再利用生成对抗网络的判别器思想去训练这个输出的图片。使用L2 loss和adversarial loss作为损失函数,L2 loss可以使得mask里面的内容被恢复,adversarial loss则使得恢复的内容显得更加真实。【其实还是一个GAN的结果,只不过生成器用Encoder和Decoder代...
Nowadays, image inpainting methods based on deep learning would lead to information loss when acquiring deep features, which is not conducive to the restoration of texture details and ignores the inpainting of semantic features. Besides, great majority o
Code Edit researchmm/AOT-GAN-for-Inpainting official 400 zyddnys/manga-image-translator 4,679 Tasks Edit Image Inpainting Texture Synthesis Vocal Bursts Intensity Prediction Datasets Edit ImageNet Places CelebA-HQ Results from the Paper Edit Ranked #9 on Image Inpainting on Places2 Get...
Places: A 10 million image database for scene recognition IEEE Trans. Pattern Anal. Mach. Intell. (2018) D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, A.A. Efros, Context Encoders: Feature Learning by Inpainting, in:...View more references Cited by (30) SANet: Face super-res...
作者在人脸图像、自然图像、纹理图像等测试集上,都产生了比现在已有的方法更好的效果 数据库 Places2 datasets 是一个场景图像数据集,包含 1千万张 图片,400多个不同类型的场景环境,可用于以场景和环境为应用内容的视觉认知任务。 CeleA是香港中文大学的开放数据,包含10177个名人身份的202599张图片,并且都做好了特征...