From Textural Inpainting to Deep Generative Models: An Extensive Survey of Image Inpainting Techniques
- 1 Amritsar College of Engineering and Technology, India
- 2 Panjab University, India
- 3 Chandigarh University, India
- 4 NTNU - Norwegian University of Science and Technology, Norway
Copyright: © 2020 Setika Mehra, Ayush Dogra, Bhawna Goyal, Apoorav Maulik Sharma and Ramesh Chandra. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Image inpainting is an evolving discipline of image processing with the objective of reconstructing an image by removing unwanted information, adding missing information or presenting the information appealing to the human visual system. In the presented manuscript, we have exhibited an extensive survey of various image inpainting techniques. The effectiveness of the techniques is together summarized with significant comparisons and assessed by analyzing the merits and demerits. For applicability of image inpainting imparting optimum results in the field of loss concealment, object removal, image restoration or disocclusion, the information from nearby regions is seeked to acquire an image with restored absent information. The inpainted image result can be evaluated using subjective and objective analysis, with emphasis on subjective analysis as a dedicated tool for evaluation.
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- Image Inpainting
- Structural Inpainting
- Textural Inpainting
- Partial Differential Equation (PDE)
- Exemplar-Based Inpainting
- Deep Generative Models