Spatially adaptive high-resolution image reconstruction of low-resolution DCT-based compressed images

Sung Cheol Park, Moon Gi Kang, C. Andrew Segall, Aggelos K. Katsaggelos

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed images is considered in this paper. The presence of the compression system complicates the recovery problem, as the operation reduces the amount of frequency aliasing in the low-resolution frames and introduces a non-linear quantization process. The effect of the quantization error and resulting inaccurate sub-pixel motion information is modeled as a zero-mean additive correlated Gaussian noise. A regularization functional is introduced not only to reflect the relative amount of registration error in each low-resolution image but also to determine the regularization parameter without any prior knowledge in the reconstruction procedure. The effectiveness of the proposed algorithm is demonstrated experimentally.

Original languageEnglish
PagesII/861-II/864
Publication statusPublished - 2002 Jan 1
EventInternational Conference on Image Processing (ICIP'02) - Rochester, NY, United States
Duration: 2002 Sep 222002 Sep 25

Other

OtherInternational Conference on Image Processing (ICIP'02)
CountryUnited States
CityRochester, NY
Period02/9/2202/9/25

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All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Park, S. C., Kang, M. G., Segall, C. A., & Katsaggelos, A. K. (2002). Spatially adaptive high-resolution image reconstruction of low-resolution DCT-based compressed images. II/861-II/864. Paper presented at International Conference on Image Processing (ICIP'02), Rochester, NY, United States.