Improved Gradient Threshold Image Sharpness Evaluation Algorithm

The key step of digital image technology to realize autofocus is effective image sharpness evaluation. Aiming at the problems of poor anti-noise and low real-time performance of traditional gray gradient algorithms, an improved sharpness evaluation algorithm is proposed. First, the image adaptive segmentation threshold is calculated by the OSTU method and the global variance. Then, the adaptive segmentation threshold and the local variance of the image pixels are compared to extract the edge pixels in the entire image. Finally, considering the characteristics of human vision, the multi-direction Tenengrad operator is used to evaluate the image, and then the evaluation operation values of the edge pixels in the image are superimposed to obtain the quantized value of the image sharpness. In order to measure the performance of the improved algorithm, it is compared with the traditional gray gradient algorithm. The experimental results show that compared with the traditional gray gradient algorithm, the proposed algorithm has the advantages of high real-time performance, high sensitivity, and good anti-noise ability.