Fast Computation of Image Sharpness Evaluation Function Based on Pixel Classification

This text presents a method for rapidly computing image sharpness evaluation through pixel classification. By categorizing image pixels based on their characteristics and statistical properties, this approach enables more accurate assessment of image clarity. The implementation typically involves preprocessing steps such as edge detection and texture analysis, followed by classification algorithms that group pixels into relevant categories. This methodology finds applications in various image processing tasks including image enhancement, quality assessment, and automated focusing systems. The key advantage lies in its computational efficiency, achieved through optimized classification algorithms that reduce processing time while maintaining accuracy. By utilizing this evaluation function, practitioners can effectively quantify image sharpness metrics and enhance performance in digital image processing applications. The algorithm can be implemented using common computer vision libraries with functions for feature extraction and machine learning-based classification.