Image Inpainting Using Nonlocal Texture Matching and Nonlinear Filtering

By Ding Ding, Sundaresh Ram, Jeffrey J. Rodríguez. 2019

155 citations recorded by the source corpus.

Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch. To compute the pixel intensity, we apply the $\alpha $ -trimmed mean filter to the candidate patches to inpaint the target patch pixel-by-pixel. The proposed algorithm is compared with four current image inpainting algorithms under different scenarios, including object removal, texture synthesis, and error concealment. Experimental results show that the proposed algorithm outperforms the existing algorithms when inpainting large missing regions in images with texture and geometric structures.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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Citations in the corpus

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021, background): “…on hand-engineered multi-stage architectures, specialized operations, or intermediate structures like edges or contours (Altinel et al., 2018; Ding et al., 2018; Iizuka et al., 2017; Jiang et al., 2019; Lahiri et al., 2020; Li et al., 2020; Liu et al., 2018; 2019a; 2020; Nazeri et al., 2019;…”
Contrastive Learning for Diverse Disentangled Foreground Generation (2022, background): “Recently, deep learning methods [8,18,33,35,48,50,52,53,55,59,28,57,61] dramatically boosted the quality, in terms of both visual quality and semantic coherence.”
Image Fine-grained Inpainting (2020, unknown): “[22] proposed a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch.”