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Image multidistortion estimation.

André L Caron1, Pierre-Marc Jodoin

  • 1MOIVRE Research Center, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada. pierre-marc.jodoin@usherbrooke.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 11, 2011
PubMed
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This study introduces a novel method using multiscale structural similarity (MS-SSIM) to accurately estimate image noise and blur. The technique efficiently recovers distortion parameters, achieving high accuracy for both noise and blur estimation in degraded images.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Image distortion estimation is crucial for various applications.
  • Existing methods often struggle with simultaneous noise and blur estimation.
  • The multiscale structural similarity (MS-SSIM) framework is primarily used for image quality assessment.

Purpose of the Study:

  • To develop a robust method for estimating noise and blur in distorted images.
  • To leverage the MS-SSIM framework for distortion parameter recovery.
  • To compare the proposed method against state-of-the-art techniques.

Main Methods:

  • Utilizing the MS-SSIM framework to establish a bijective mapping between noise/blur space and MS-SSIM space.
  • Formulating multidistortion estimation as an optimization problem.

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  • Employing search strategies like Newton, simplex, NewUOA, and brute-force search.
  • Approximating the bijective mapping using a bicubic patch for efficiency.

    • Main Results:

    • A bijective mapping was identified, enabling distortion parameter recovery.
    • The bicubic patch approximation reduced processing time by 40x with minimal precision loss.
    • Accurate estimation of blur (approx. 2%) and noise (approx. 8%) was achieved.
    • The proposed method demonstrated competitive performance against four state-of-the-art techniques.

    Conclusions:

    • The MS-SSIM framework can be effectively adapted for simultaneous noise and blur estimation.
    • The proposed optimization-based approach offers an efficient and accurate solution for image distortion recovery.
    • The use of a bicubic patch significantly enhances computational efficiency without compromising accuracy.