1 research outputs found
Image inpainting using frequency domain priors
In this paper, we present a novel image inpainting technique using frequency
domain information. Prior works on image inpainting predict the missing pixels
by training neural networks using only the spatial domain information. However,
these methods still struggle to reconstruct high-frequency details for real
complex scenes, leading to a discrepancy in color, boundary artifacts,
distorted patterns, and blurry textures. To alleviate these problems, we
investigate if it is possible to obtain better performance by training the
networks using frequency domain information (Discrete Fourier Transform) along
with the spatial domain information. To this end, we propose a frequency-based
deconvolution module that enables the network to learn the global context while
selectively reconstructing the high-frequency components. We evaluate our
proposed method on the publicly available datasets CelebA, Paris Streetview,
and DTD texture dataset, and show that our method outperforms current
state-of-the-art image inpainting techniques both qualitatively and
quantitatively