Image Inpainting Based on Structural Tensor Edge Intensity Model
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Inpainting Based on Structural Tensor Edge Intensity Model Jing Wang Yan-Hong Zhou Hai-Feng Sima Zhan-Qiang Huo Ai-Zhong Mi College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China
Abstract: In the exemplar-based image inpainting approach, there are usually two major problems: the unreasonable calculation of priority and only considering the color features in the patch lookup strategy. In this paper, we propose an image inpainting approach based on the structural tensor edge intensity model. First, we use the progressive scanning inpainting method to avoid the image filling order being affected by the priority function. Then, we use the edge intensity model to build the patches similarity function for correctly identifying the local image structure. Finally, the balance operator is used to restrict the excessive propagation of structural information to ensure the correct structural reconstruction. The experimental results show that the our approach is comparable and even superior to some state-of-the-art inpainting algorithms. Keywords: Exemplar-based technique, image inpainting, structural tensor, edge intensity model, structure propagation, balance operator.
1 Introduction Image restoration (i.e., image de-noising, non-blind deblurring and image inpainting)[1] can help recover different kinds of images. Among them, image inpainting refers to using the reliable information remaining in the image to recover the target area (missing or damaged parts). The inpainting technique relies on the color and structural information in the image, ensuring the reconstructed image is visually reasonable. Now that digital images have become a part of our lives, people have more requirements for images, i.e., restoring the images that have been damaged due to improper storage and removing unnecessary parts. This technique also plays an important role in the post-processing of videos and movies, i.e., removing watermarks and recovering damaged vintage films[2, 3]. Because of the wide application of image inpainting, this technology has attracted the attention of a large number of researchers. It is also an important task in the field of computer vision. In recent years, image inpainting has made great progress, using methods based on partial differential equations (PDEs), exemplar-based techniques and methods based on sparse representations. The partial differential equation image inpainting algorithm[4] is a method based on thermal diffusion, of which the curvature-driven diffusions (CDD) model[5] and the total variation (TV) model[6] are two typical ones. Li et al.[7] proposed an im Research Article Manuscript received June 13, 2020; accepted September 16, 2020 Recommended by Associate Editor De Xu © Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2020
proved TV algorithm in which the calculation of the diffusion coefficient is determ
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