Literature DB >> 25473254

Single Image Super-resolution using Deformable Patches.

Yu Zhu1, Yanning Zhang1, Alan L Yuille2.   

Abstract

We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.

Entities:  

Year:  2014        PMID: 25473254      PMCID: PMC4249591          DOI: 10.1109/CVPR.2014.373

Source DB:  PubMed          Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit        ISSN: 1063-6919


  6 in total

1.  Fast and robust multiframe super resolution.

Authors:  Sina Farsiu; M Dirk Robinson; Michael Elad; Peyman Milanfar
Journal:  IEEE Trans Image Process       Date:  2004-10       Impact factor: 10.856

2.  Coupled dictionary training for image super-resolution.

Authors:  Jianchao Yang; Zhaowen Wang; Zhe Lin; Scott Cohen; Thomas Huang
Journal:  IEEE Trans Image Process       Date:  2012-04-03       Impact factor: 10.856

3.  Image super-resolution with sparse neighbor embedding.

Authors:  Xinbo Gao; Kaibing Zhang; Dacheng Tao; Xuelong Li
Journal:  IEEE Trans Image Process       Date:  2012-03-09       Impact factor: 10.856

4.  Image super-resolution via sparse representation.

Authors:  Jianchao Yang; John Wright; Thomas S Huang; Yi Ma
Journal:  IEEE Trans Image Process       Date:  2010-05-18       Impact factor: 10.856

5.  Super-resolution without explicit subpixel motion estimation.

Authors:  Hiroyuki Takeda; Peyman Milanfar; Matan Protter; Michael Elad
Journal:  IEEE Trans Image Process       Date:  2009-05-26       Impact factor: 10.856

6.  Generalizing the nonlocal-means to super-resolution reconstruction.

Authors:  Matan Protter; Michael Elad; Hiroyuki Takeda; Peyman Milanfar
Journal:  IEEE Trans Image Process       Date:  2009-01       Impact factor: 10.856

  6 in total
  1 in total

1.  Impact of color augmentation and tissue type in deep learning for hematoxylin and eosin image super resolution.

Authors:  Cyrus Manuel; Philip Zehnder; Sertan Kaya; Ruth Sullivan; Fangyao Hu
Journal:  J Pathol Inform       Date:  2022-10-01
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.