Image Inpainting with Frequency Domain Wavelet Convolution
Jain-Kai Huang, Tsung-Jung Liu, Kuan-Hsien Liu · 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) · 2022
This paper used Time-Frequency Analysis (TFA) techniques for signal processing on tasks of computer vision. Our main idea is as follows: To build a simple network architecture without two or more convolutional neural networks (CNNs), ana-lyze hidden features by Discrete Wavelet Transform (DWT), and send them into filters as weights by convolutions, transformers or other methods. And we do not need to build the network with 2 or more stages to accomplish this idea. Actually, we try to directly use TFA skills on CNN to build one-stage network. Networks which build by this way not only keep their outstanding performance, but also cost lower computing resources. In this paper, we mainly use DWT on CNN to solve image inpainting problems. And the results show that our model can work stably in frequency domain to realize free-form image inpainting.