Relevant bibliographies by topics / Image outpainting

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Image outpainting'

Author: Grafiati
Published: 25 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Image outpainting"

1

Xiao, Qingguo, Guangyao Li, and Qiaochuan Chen. "Image Outpainting: Hallucinating Beyond the Image." IEEE Access 8 (2020): 173576–83. http://dx.doi.org/10.1109/access.2020.3024861.

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Wang, Yaxiong, Yunchao Wei, Xueming Qian, Li Zhu, and Yi Yang. "Sketch-Guided Scenery Image Outpainting." IEEE Transactions on Image Processing 30 (2021): 2643–55. http://dx.doi.org/10.1109/tip.2021.3054477.

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Liu, Chi-Kuang, and Hsuan-Ming Huang. "Unsupervised deep learning based image outpainting for dual-source, dual-energy computed tomography." Radiation Physics and Chemistry 188 (November 2021): 109635. http://dx.doi.org/10.1016/j.radphyschem.2021.109635.

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Singh, Shailendra, Nainish Aggarwal, Udit Jain, and Hrithik Jaiswal. "Outpainting Images and Videos using GANs." International Journal of Computer Trends and Technology 68, no. 5 (May 25, 2020): 24–29. http://dx.doi.org/10.14445/22312803/ijctt-v68i5p107.

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K., Vignesha, and Rabeeh Mohammed Ali. "Image Outpainting and Harmonization using GANs." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, May 10, 2020, 294–97. http://dx.doi.org/10.32628/cseit206370.

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Although the inherently ambiguous task of predicting what resides on the far side all four edges of a image has rarely been explored before, we have a tendency to demonstrate that GANs hold powerful potential in manufacturing reasonable extrapolations. Two outpainting ways square measure projected that aim to instigate this line of research: the primary approach uses a context encoder inspired by common inpainting architectures and paradigms, whereas the second approach adds an extra post-processing step using a single-image generative model. This way, the hallucinated details are integrated with the design of the original image.
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Dissertations / Theses on the topic "Image outpainting"

1

Mennborg, Alexander. "AI-Driven Image Manipulation : Image Outpainting Applied on Fashion Images." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85148.

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The e-commerce industry frequently has to deal with displaying product images in a website where the images are provided by the selling partners. The images in question can have drastically different aspect ratios and resolutions which makes it harder to present them while maintaining a coherent user experience. Manipulating images by cropping can sometimes result in parts of the foreground (i.e. product or person within the image) to be cut off. Image outpainting is a technique that allows images to be extended past its boundaries and can be used to alter the aspect ratio of images. Together with object detection for locating the foreground makes it possible to manipulate images without sacrificing parts of the foreground. For image outpainting a deep learning model was trained on product images that can extend images by at least 25%. The model achieves 8.29 FID score, 44.29 PSNR score and 39.95 BRISQUE score. For testing this solution in practice a simple image manipulation pipeline was created which uses image outpainting when needed and it shows promising results. Images can be manipulated in under a second running on ZOTAC GeForce RTX 3060 (12GB) GPU and a few seconds running on a Intel Core i7-8700K (16GB) CPU. There is also a special case of images where the background has been digitally replaced with a solid color and they can be outpainted even faster without deep learning.
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Conference papers on the topic "Image outpainting"

1

Tan, Cheng-Yo, Chiao-An Yang, Shang-Fu Chen, Meng-Lin Wu, and Yu-Chiang Frank Wang. "Robust Image Outpainting With Learnable Image Margins." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506634.

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Lin, Han, Maurice Pagnucco, and Yang Song. "Edge Guided Progressively Generative Image Outpainting." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00090.

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Zhang, Lingzhi, Jiancong Wang, and Jianbo Shi. "Multimodal Image Outpainting with Regularized Normalized Diversification." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093636.

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Xu, Shuzhen, Jin Wang, and Qing Zhu. "Gradual Image Outpainting with Pixel to Pixel Mapping." In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). IEEE, 2019. http://dx.doi.org/10.1109/eitce47263.2019.9094870.

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Yang, Zongxin, Jian Dong, Ping Liu, Yi Yang, and Shuicheng Yan. "Very Long Natural Scenery Image Prediction by Outpainting." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.01066.

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Kim, Kyunghun, Yeohun Yun, Keon-Woo Kang, Kyeongbo Kong, Siyeong Lee, and Suk-Ju Kang. "Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning." In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2021. http://dx.doi.org/10.1109/wacv48630.2021.00217.

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