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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

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Parameter estimation for blind and non-blind deblurring using residual whiteness measures.

Mariana S C Almeida¹, Mario A T Figueiredo

¹Instituto de Telecomunicações, Instituto Superior Técnico, 1049-001 Lisboa, Portugal. mariana.almeida@lx.it.pt

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 18, 2013

Summary

This study introduces new criteria for image deblurring (ID) algorithms. By analyzing the spectral whiteness of residual images, these methods optimize regularization parameters and iteration counts for better results.

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Image deblurring (ID) is an ill-posed problem.
Traditional ID methods rely on regularization and optimization.
Algorithm performance hinges on regularization parameters and iteration count.

Purpose of the Study:

Propose novel criteria for adjusting regularization parameters and iteration counts in ID algorithms.
Develop methods applicable to both blind and non-blind ID.
Improve the performance and robustness of ID techniques.

Main Methods:

Formulate ID as an optimization problem with data and regularization terms.
Propose whiteness-based criteria analyzing the spectral properties of residual images.
Apply criteria to blind ID algorithms using continuation and non-blind ID.

Main Results:

Whiteness-based criteria effectively adjust regularization and stopping points.
Achieve signal-to-noise ratio (SNR) improvements close to optimal (0.15 dB below clairvoyant).
Demonstrate competitive performance against state-of-the-art methods in non-blind ID.

Conclusions:

The proposed whiteness-based criteria offer a robust approach for ID parameter tuning.
These criteria are particularly effective for blind ID algorithms.
The methods provide significant performance gains and are competitive with existing techniques.