Estimating degradation model parameters using neighborhood pattern distributions: an optimization approach

Area of Science:

• Computer Vision
• Image Processing
• Computational Imaging

Background:

• Noise models are essential for image restoration, synthetic data generation, and performance prediction.
• Existing research primarily addresses model calibration, leaving general estimation problems under-explored.
• General estimation requires inferring model parameters from degraded images alone.

Purpose of the Study:

• To develop a parameter estimation algorithm for a morphological, binary, page-level image degradation model.
• To address the under-researched problem of general parameter estimation for image degradation models.
• To enable more accurate image restoration and synthetic data generation.

Main Methods:

• A novel parameter estimation algorithm for morphological, binary, page-level image degradation.
• Input: degraded image and font type information (italic, bold, serif, sans serif).
• Parameter search using direct search optimization, comparing local neighborhood pattern distributions via Kolmogorov-Smirnov test p-values.

Main Results:

• The algorithm successfully estimates parameters for degraded document images.
• Demonstrated similarity between simulated and real degraded image neighborhood patterns.
• Validated the effectiveness of the direct search optimization and Kolmogorov-Smirnov test.

Conclusions:

• The proposed algorithm effectively addresses the general estimation problem for binary image degradation models.
• This advancement is vital for improving image restoration algorithms and synthetic data generation.
• The method provides a robust approach for understanding and simulating image degradation processes.