Explore MATLAB source code curated for "清晰度" with clean implementations, documentation, and examples.
This algorithm evaluates image sharpness by calculating the statistical information entropy of images - sharp images exhibit higher entropy values while blurred images show lower entropy measurements, with implementation typically involving histogram calculation and probability distribution analysis.
Based on the contrast sensitivity from EF characteristics (spatial frequency response curve), this method performs 2D multi-level wavelet decomposition on images. From the decomposed wavelet components, it extracts corresponding luminance, sharpness, and correlation metrics for each frequency band. The geometric mean of these three metrics is then arithmetically averaged with the inner product of frequency band weighting coefficients to form a comprehensive image quality evaluation metric.