Thermal Infrared Image Inpainting via Edge-Aware Guidance
- URL: http://arxiv.org/abs/2210.16000v1
- Date: Fri, 28 Oct 2022 09:06:54 GMT
- Title: Thermal Infrared Image Inpainting via Edge-Aware Guidance
- Authors: Zeyu Wang, Haibin Shen, Changyou Men, Quan Sun, Kejie Huang
- Abstract summary: In this paper, we propose a novel task -- Thermal Infrared Image Inpainting.
We adopt the edge generator to complete the canny edges of broken TIR images.
The completed edges are projected to the normalization weights and biases to enhance edge awareness of the model.
Experiments demonstrate that our method outperforms state-of-the-art image inpainting approaches on FLIR thermal dataset.
- Score: 8.630992878659084
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Image inpainting has achieved fundamental advances with deep learning.
However, almost all existing inpainting methods aim to process natural images,
while few target Thermal Infrared (TIR) images, which have widespread
applications. When applied to TIR images, conventional inpainting methods
usually generate distorted or blurry content. In this paper, we propose a novel
task -- Thermal Infrared Image Inpainting, which aims to reconstruct missing
regions of TIR images. Crucially, we propose a novel deep-learning-based model
TIR-Fill. We adopt the edge generator to complete the canny edges of broken TIR
images. The completed edges are projected to the normalization weights and
biases to enhance edge awareness of the model. In addition, a refinement
network based on gated convolution is employed to improve TIR image
consistency. The experiments demonstrate that our method outperforms
state-of-the-art image inpainting approaches on FLIR thermal dataset.
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