Image Sharpness Evaluation Algorithm Based on Statistical Image Information Entropy

This algorithm evaluates image sharpness by statistically analyzing the information entropy of images. Sharp images typically demonstrate higher information entropy, whereas blurred images exhibit lower entropy values. Information entropy serves as a quantitative measure of image complexity and information content, reflecting the distribution of pixel values and texture details within the image. Through computational implementation using probability distribution functions and histogram analysis, we can determine the image's sharpness and detail level. Key steps in the algorithm implementation include calculating pixel value probabilities, applying the entropy formula H = -Σ(p(x) * log2(p(x))), and comparing the resulting entropy value against established thresholds. Consequently, this algorithm enables accurate assessment of image sharpness through entropy analysis, providing quantitative judgment of image quality for applications in computer vision and image processing systems.