Context-based adaptive image resolution upconversion

Guangming Shi, Weisheng Dong, Xiaolin Wu, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


We propose a practical context-based adaptive image resolution upconversion algorithm. The basic idea is to use a low-resolution (LR) image patch as a context in which the missing high-resolution (HR) pixels are estimated. The context is quantized into classes and for each class an adaptive linear filter is designed using a training set. The training set incorporates the prior knowledge of the point spread function, edges, textures, smooth shades, etc. into the upconversion filter design. For low complexity, two 1-D context-based adaptive interpolators are used to generate the estimates of the missing pixels in two perpendicular directions. The two directional estimates are fused by linear minimum mean-squares weighting to obtain a more robust estimate. Upon the recovery of the missing HR pixels, an efficient spatial deconvolution is proposed to deblur the observed LR image. Also, an iterative upconversion step is performed to further improve the upconverted image. Experimental results show that the proposed context-based adaptive resolution upconverter performs better than the existing methods in both peak SNR and visual quality.
Original languageEnglish
Article number013008
JournalJournal of Electronic Imaging
Issue number1
Publication statusPublished - 5 Oct 2010

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Context-based adaptive image resolution upconversion'. Together they form a unique fingerprint.

Cite this