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

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

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed observations is considered in this paper. The introduction of compression complicates the recovery problem. We analyze the DCT quantization noise and propose to model it in the spatial domain as a colored Gaussian process. This allows us to estimate the quantization noise at low bit-rates without explicit knowledge of the original image frame, and we propose a method that simultaneously estimates the quantization noise along with the high resolution data. We also incorporate a nonstationary image prior model to address blocking and ringing artifacts while still preserving edges. To facilitate the simultaneous estimate, we employ a regularization functional to determine the regularization parameter without any prior knowledge of the reconstruction procedure. The smoothing functional to be minimized is then formulated to have a global minimizer in spite of its nonlinearity by enforcing convergence and convexity requirements. Experiments illustrate the benefit of the proposed method when compared to traditional high-resolution image reconstruction methods. Quantitative and qualitative comparisons are provided.

Original languageEnglish
Pages (from-to)573-585
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number4
Publication statusPublished - 2004 Apr

Bibliographical note

Funding Information:
Manuscript received April 4, 2002; revised August 1, 2003. This work was supported by the Korea Science and Engineering Foundation (KOSEF) through Biometrics Engineering Research Center (BERC) at Yonsei University. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Mario A. T. Figueiredo.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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