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Fast image/video upsampling

Published: 01 December 2008 Publication History

Abstract

We propose a simple but effective upsampling method for automatically enhancing the image/video resolution, while preserving the essential structural information. The main advantage of our method lies in a feedback-control framework which faithfully recovers the high-resolution image information from the input data, without imposing additional local structure constraints learned from other examples. This makes our method independent of the quality and number of the selected examples, which are issues typical of learning-based algorithms, while producing high-quality results without observable unsightly artifacts. Another advantage is that our method naturally extends to video upsampling, where the temporal coherence is maintained automatically. Finally, our method runs very fast. We demonstrate the effectiveness of our algorithm by experimenting with different image/video data.

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Published In

cover image ACM Conferences
SIGGRAPH Asia '08: ACM SIGGRAPH Asia 2008 papers
December 2008
581 pages
ISBN:9781450318310
DOI:10.1145/1457515
  • Editor:
  • John C. Hart
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 December 2008

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Author Tags

  1. image deconvolution
  2. image/video enhancement
  3. image/video upsampling

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SIGGRAPH Asia '08 Paper Acceptance Rate 59 of 320 submissions, 18%;
Overall Acceptance Rate 178 of 869 submissions, 20%

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  • (2024)Design of a super-resolution scheme for plenoptic imagesJournal of Optics10.1007/s12596-024-02116-1Online publication date: 15-Aug-2024
  • (2022)Data Acquisition and Preparation for Dual-Reference Deep Learning of Image Super-ResolutionIEEE Transactions on Image Processing10.1109/TIP.2022.318481931(4393-4404)Online publication date: 2022
  • (2022)Digital Heritage Reconstruction Using Super-resolution and InpaintingundefinedOnline publication date: 9-Apr-2022
  • (2021)Robust Real-World Image Super-Resolution against Adversarial AttacksProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475627(5148-5157)Online publication date: 17-Oct-2021
  • (2021)Toward reconfigurable kernel datapaths with learned optimizationsProceedings of the Workshop on Hot Topics in Operating Systems10.1145/3458336.3465288(175-182)Online publication date: 1-Jun-2021
  • (2020)Deep generative adversarial network to enhance image quality for fast object detection in construction sitesProceedings of the Winter Simulation Conference10.5555/3466184.3466463(2447-2459)Online publication date: 14-Dec-2020
  • (2020)An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial NetworkIEEE Access10.1109/ACCESS.2020.29723008(29027-29039)Online publication date: 2020
  • (2019)Survey on Single Image based Super-resolution — Implementation Challenges and SolutionsMultimedia Tools and Applications10.1007/s11042-019-08254-079:3-4(1641-1672)Online publication date: 7-Nov-2019
  • (2019)Accelerate neural style transfer with super-resolutionMultimedia Tools and Applications10.1007/s11042-018-6929-x79:7-8(4347-4364)Online publication date: 12-Jan-2019
  • (2018)Single-Image Super-Resolution Using Gradient and Texture SimilarityIntegrated Intelligent Computing, Communication and Security10.1007/978-981-10-8797-4_63(621-630)Online publication date: 15-Sep-2018
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