IEEE TIP (2001)īarnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: Patchmatch: a randomized correspondence algorithm for structural image editing. Dataset and code are available at: īallester, C., Bertalmio, M., Caselles, V., Sapiro, G., Verdera, J.: Filling-in by joint interpolation of vector fields and gray levels. Extensive experiments demonstrate the consistent decrease of artifact regions and inpainting quality improvement across the different methods. Finally, we further apply the generated masks for iterative image inpainting by combining our approach with multiple recent inpainting methods. PAR demonstrates a strong correlation with real user preference. Second, we propose a new interpretable evaluation metric called Perceptual Artifact Ratio (PAR), which is the ratio of objectionable inpainted regions to the entire inpainted area. ![]() ![]() Then we train advanced segmentation networks on this dataset to reliably localize inpainting artifacts within inpainted images. Specifically, we first construct a new inpainting artifacts dataset by manually annotating perceptual artifacts in the results of state-of-the-art inpainting models. Inspired by this workflow, we propose a new learning task of automatic segmentation of inpainting perceptual artifacts, and apply the model for inpainting model evaluation and iterative refinement. Users perceive these artifacts to judge the effectiveness of inpainting models, and retouch these imperfect areas to inpaint again in a typical retouching workflow. Deep GAN-based models greatly improve the inpainting performance in structures and textures within the hole, but might also generate unexpected artifacts like broken structures or color blobs. Image inpainting is an essential task for multiple practical applications like object removal and image editing.
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