# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 1 Introduction  
With the significant advantages of non-contact, flexibility, and high integration, computer vision measurement has broad application prospects in electronic semiconductors, automotive manufacturing, food packaging, film, and other industrial fields. Image sharpness is the core index to measure the quality of visual images; therefore, the research on the evaluation method of visual image sharpness is one of the key technologies to achieve visual detection [1, 2, 3]. Moreover, as people demand more and more sharpness in video chats, HDTV, etc., the research of a more efficient image sharpness evaluation method has become a pressing problem nowadays. Generally, image sharpness evaluation methods can be divided into full-reference (FR) sharpness evaluation methods, reduced-reference (RR) sharpness evaluation methods, and no-reference (NR) sharpness evaluation methods. Among them, the FR sharpness evaluation methods are used to judge the degree of deviation of the measured image from the sharp reference image [4]. The RR sharpness evaluation methods evaluate the measured image by extracting only part of the information of the reference image [5]. However, in practical applications, undistorted sharp reference images are usually difficult to obtain. Therefore, the NR sharpness evaluation methods have higher research value and wider application capability. Existing NR sharpness evaluation methods are formulated either in the transform domain or in the spatial domain [6]. Transform domain-based methods [7, 8, 9, 10] need to transform images from the spatial domain to other domains forprocessing. However, the computational complexity is often too large. Therefore, such methods are poor in real time and limited in many applications. Spatial domain-based methods [11; 12; 13; 14; 15] can be divided into two main types. One type is based on the fact that clear images have higher contrast compared to blurred images. Typical evaluation methods of this type are the various gradient function methods, such as the Tenegrad function method and energy gradient function method [11]. The other type is based on the fact that image blurring will lead to edge diffusion; a typical evaluation method of this type is the average edge width method [12]. It should be noted that, although both the contrast-based evaluation method and the edge information-based evaluation method have the advantage of low computational complexity, the former is more dependent on the image content compared to the latter, that is, the former method tends to fail when the contents of measured images are different. [PERSON] et al. [13] proposed a no-reference image sharpness evaluation method on scanning electron microscopes. The method firstly extracts the edge of dark channel maps by a Sobel operator. It then removes the noise effect but preserves the edge information by an edge-preserving operator based on the weighted least squares' (WLS) framework. Finally, it combines the maximum gradient of each edge point with the average gradient to form the sharpness evaluation index. Although this method extracts a part of the edge information of the image by edge detection, it is still essentially an evaluation method based on the contrast principle. [PERSON] [14] proposed an image sharpness evaluation method based on a strong edge width. She convolved the measured image by a Sobel operator to obtain the horizontal and vertical gradient maps, respectively. By selecting the threshold, the horizontal and vertical strong edge points of the measured image were obtained. Moreover, the strong edge width was solved. Finally, the sharpness evaluation index was generated by introducing the histogram information. In summary, most of the current image sharpness evaluation methods based on edge information still extract edge points by a Sobel operator and often only consider horizontal and vertical directions when determining the edge direction of edge points, which largely limits the further improvement of the accuracy of this type of evaluation methods.