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Single image super-resolution with non-local means and steering kernel regression.

Kaibing Zhang1, Xinbo Gao, Dacheng Tao

  • 1School of Electronic Engineering, Xidian University, Xi’an 710071, China. kbzhang0505@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 26, 2012
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Summary

This study introduces a new image super-resolution (SR) method using non-local and local image priors. The approach enhances image quality by leveraging patch redundancy and neighbor pixel information for better reconstruction.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image super-resolution (SR) is an ill-posed problem requiring effective regularization priors.
  • Existing methods often focus on either local or non-local image properties.

Purpose of the Study:

  • To develop a novel image SR method by integrating both non-local and local regularization priors.
  • To improve the quantitative and perceptual quality of reconstructed super-resolved images.

Main Methods:

  • Learning a non-local prior using the non-local means filter, exploiting similar patch redundancy.
  • Learning a local prior using steering kernel regression, estimating pixels from neighbors.
  • Developing a maximum a posteriori (MAP) probability framework combining both priors for SR recovery.

Main Results:

  • The proposed method effectively reconstructs higher quality super-resolved images.
  • Experimental results demonstrate significant improvements in both quantitative metrics and perceptual quality.
  • The integration of complementary local and non-local priors leads to superior SR performance.

Conclusions:

  • The novel image SR method effectively addresses the ill-posed nature of the problem by combining diverse regularization priors.
  • The proposed MAP framework with learned non-local and local priors offers a robust approach for high-quality image super-resolution.
  • This work advances the field of image SR by providing a more comprehensive regularization strategy.