修补
人工智能
计算机科学
规范化(社会学)
像素
卷积神经网络
模式识别(心理学)
卷积(计算机科学)
特征提取
深度学习
计算机视觉
人工神经网络
图像(数学)
人类学
社会学
作者
Ning Wang,Yipeng Zhang,Lefei Zhang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:30: 1784-1798
被引量:114
标识
DOI:10.1109/tip.2020.3048629
摘要
Image inpainting is a challenging computer vision task that aims to fill in missing regions of corrupted images with realistic contents. With the development of convolutional neural networks, many deep learning models have been proposed to solve image inpainting issues by learning information from a large amount of data. In particular, existing algorithms usually follow an encoding and decoding network architecture in which some operations with standard schemes are employed, such as static convolution, which only considers pixels with fixed grids, and the monotonous normalization style (e.g., batch normalization). However, these techniques are not well-suited for the image inpainting task because the random corrupted regions in the input images tend to mislead the inpainting process and generate unreasonable content. In this paper, we propose a novel dynamic selection network (DSNet) to solve this problem in image inpainting tasks. The principal idea of the proposed DSNet is to distinguish the corrupted region from the valid ones throughout the entire network architecture, which may help make full use of the information in the known area. Specifically, the proposed DSNet has two novel dynamic selection modules, namely, the validness migratable convolution (VMC) and regional composite normalization (RCN) modules, which share a dynamic selection mechanism that helps utilize valid pixels better. By replacing vanilla convolution with the VMC module, spatial sampling locations are dynamically selected in the convolution phase, resulting in a more flexible feature extraction process. Besides, the RCN module not only combines several normalization methods but also normalizes the feature regions selectively. Therefore, the proposed DSNet can illustrate realistic and fine-detailed images by adaptively selecting features and normalization styles. Experimental results on three public datasets show that our proposed method outperforms state-of-the-art methods both quantitatively and qualitatively.
科研通智能强力驱动
Strongly Powered by AbleSci AI