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Related Experiment Video

Updated: Jun 12, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Automatic parameter selection for denoising algorithms using a no-reference measure of image content.

Xiang Zhu1, Peyman Milanfar

  • 1Department of Electrical Engineering, University of California, Santa Cruz, 95064, USA. xzhu@soe.ucsc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new no-reference image quality metric, Q, for parameter tuning in image processing. Metric Q is easy to compute, robust to noise, and correlates well with human perception.

Related Experiment Videos

Last Updated: Jun 12, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Parameter selection in image processing algorithms is often empirical.
  • Existing methods like cross-validation and SURE have limitations regarding noise assumptions and computational cost.

Purpose of the Study:

  • To propose a novel no-reference image quality metric (Q) for automatic parameter tuning.
  • To provide a quantitative measure of image content (sharpness, contrast) robust to noise and distortions.

Main Methods:

  • Developed a no-reference metric Q based on singular value decomposition of local image gradient matrices.
  • Applied metric Q to automatically set parameters for image denoising algorithms.

Main Results:

  • Metric Q is computationally efficient and reacts well to blur and noise.
  • The metric performs effectively even with non-Gaussian noise.
  • Experiments show Q correlates well with subjective quality evaluations (TID2008 database).

Conclusions:

  • The proposed metric Q offers an effective and robust solution for parameter tuning in image processing.
  • Metric Q provides a reliable objective measure of image quality, comparable to subjective assessments.