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Image inpainting
Open accessAbstract
Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them. The fill-in is done in such a way that isophote lines arriving at the regions' boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In addition, no limitations are imposed on the topology of the region to be inpainted. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like dates, subtitles, or publicity; and the removal of entire objects from the image like microphones or wires in special effects.
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Citations
Cited by
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529
“2713099 problems in imaging including denoising [1]–[3], deconvolution [4], [5], and interpolation [6].”
BackgroundImage Inpainting for Irregular Holes Using Partial Convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419
“Non-learning approaches to image inpainting rely on propagating appearance information from neighboring pixels to the target region using some mechanisms like distance field[3, 1, 30].”
BackgroundGlobally and locally consistent image completion
Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073659 · Cited by 2,203
An Iterative Regularization Method for Total Variation-Based Image Restoration
Osher, S., Burger, M., Goldfarb, D., Xu, J., & Yin, W. (2005) · Multiscale Modeling & Simulation · doi:10.1137/040605412 · Cited by 1,787
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882269 · Cited by 1,773
Sparse Representation for Color Image Restoration
Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716
Self-citationPalette: Image-to-Image Diffusion Models
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., & Norouzi, M. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530757 · Cited by 1,717
An Image Inpainting Technique Based on the Fast Marching Method
Telea, A. (2004) · Journal of Graphics Tools · doi:10.1080/10867651.2004.10487596 · Cited by 1,421
Roth, S., & Black, M. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0197-6 · Cited by 847
Self-Supervised Feature Learning by Learning to Spot Artifacts
Jenni, S., & Favaro, P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00289 · Cited by 115
Removal of adherent waterdrops from images acquired with stereo camera
Yamashita, A., Tanaka, Y., & Kaneko, T. (2005) · 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2005.1545103 · Cited by 55
A virtual wiper - restoration of deteriorated images by using multiple cameras
Yamashita, A., Kuramoto, M., Kaneko, T., & Miura, K. T. (2004) · Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453) · doi:10.1109/iros.2003.1249637 · Cited by 32
Raindrop detection and removal using salient visual features
Wu, Q., Zhang, W., & Vijaya Kumar, B. V. K. (2012) · 2012 19th IEEE International Conference on Image Processing · doi:10.1109/icip.2012.6467016 · Cited by 27
A Review of Detection and Removal of Raindrops in Automotive Vision Systems
Hamzeh, Y., & Rawashdeh, S. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7030052 · Cited by 22
“[32] was then used to recover rainy segments to form the two images.”
MethodRemoval of adherent noises from image sequences by spatio-temporal image processing
Yamashita, A., Fukuchi, I., Kaneko, T., & Miura, K. T. (2008) · 2008 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2008.4543570 · Cited by 21
Deep Learning-Based X-ray Inpainting for Improving Spinal 2d-3d Registration
Esfandiari, H., Weidert, S., Kövesházi, I., Anglin, C., Street, J., & Hodgson, A. J. (2021) · The International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2228 · Cited by 12
Metadata
- Authors
- Bertalmio, M., Sapiro, G., Caselles, V., & Ballester, C.
- Venue
- Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00
- Publisher
- ACM Press
- Published
- 2000
- Pages
- 417-424
- OpenAlex
- W2295936755
- Cited by
- 3,580
- References
- 0
- FWCI
- 28.70
- Type
- conference paper
- Language
- en
- Open access
- bronze
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