Image Quality Assessment for Performance Evaluation of Focus Measure Operators

TL;DR

This study evaluates eight focus measure operators using full-reference image quality metrics, finding that LAPD outperforms others under typical imaging conditions.

Contribution

It provides a comparative analysis of focus measure operators using multiple statistical metrics for image quality assessment.

Findings

LAPD outperforms other focus operators in evaluation.

Full-reference image quality assessment effectively compares focus measures.

Statistical metrics like MSE and PSNR are used for evaluation.

Abstract

This paper presents the performance evaluation of eight focus measure operators namely Image CURV (Curvature), GRAE (Gradient Energy), HISE (Histogram Entropy), LAPM (Modified Laplacian), LAPV (Variance of Laplacian), LAPD (Diagonal Laplacian), LAP3 (Laplacian in 3D Window) and WAVS (Sum of Wavelet Coefficients). Statistical matrics such as MSE (Mean Squared Error), PNSR (Peak Signal to Noise Ratio), SC (Structural Content), NCC (Normalized Cross Correlation), MD (Maximum Difference) and NAE (Normalized Absolute Error) are used to evaluate stated focus measures in this research. . FR (Full Reference) method of the image quality assessment is utilized in this paper. Results indicate that LAPD method is comparatively better than other seven focus operators at typical imaging conditions.