# Review: A Survey on Objective Evaluation of Image Sharpness
## 1 Introduction  
In the overview of image quality evaluation, the common evaluation indicators [1] include image noise, image color, artifacts, sharpness, etc. Image noise evaluation methods [2] mainly rely on image spatial and temporal noise, signal-to-noise ratio and grayscale noise to obtain evaluation results. The image color evaluation methods [3] usually evaluate the color degree and uniformity of the image. The image artifact evaluation methods [4] pay more attention to chromatic aberration, distortion and vignetting factors, while the image sharpness evaluation method [5] is based on the comprehensive evaluation of the edges and details of the image, which is currently one of the most popular image quality evaluation methods; it is closely related to research fields such as bionics [6], nonwoven materials [7], medicine [8], etc. According to the dependence on the reference image, the evaluation methods are divided into three types: Full-Reference (FR), Reduced-Reference (RR) and No-Reference (NR) [9]. The FR method uses the distorted image and the corresponding undistorted reference image to generate the image quality score. In the RR method, the image quality is evaluated on partial information extracted using feature extraction methods. Unlike the FR and RR methods, the NR method can use the distorted image alone to complete the quality assessment. Since it is usually impossible to obtain an undistorted image for reference in practical applications, the research on NR methods has become the current mainstream research direction [10, 11]. The image sharpness refers to the clarity of the texture and borders of various detailed parts of an image, which affects the perception of information, image acquisition, and subsequent processing, especially in some applications based on high-quality images [12, 13, 14]. The ideal image sharpness evaluation function should have the characteristics of high sensitivity, good robustness and low computational cost [15, 16, 17]. Most traditional image sharpness evaluation methods [18] are based on spatial or spectral domains. The methods in the spatial domain mainly evaluate the image by extracting the image gradient and edge information [19, 20], which have the advantages of simple calculation and high real-timeperformance but are easily disturbed by noise. The methods in the spectral domain mainly use transformation methods, such as Fourier transform and wavelet transform, to extract image frequency features for sharpness evaluation [21]. This type of method has excellent sensitivity but high computational complexity. In recent years, learning-based methods have emerged from the machine learning methods to the deep learning methods [22]. At the same time, an increasing number of evaluation methods of a combination of methods are studied and developed by scholars. A combination method is usually a new method formed by combining two or more single evaluation methods in a certain relationship. Such methods incorporate the advantages of the single method being combined and effectively improve the accuracy of quality evaluation. Although the above-reported research has obtained fruitful results, the objective evaluation standards for image sharpness are still not mature enough; few evaluation methods are suitable for most scenarios. It is unrealistic to ask one sharpness evaluation algorithm to handle all potential images due to the sophisticated and various image textures and features [23]. Therefore, this paper reviews and clusters the existing sharpness evaluation methods and conducted systematic comparative analyses on several representative methods aimed at offering directions for researchers to choose or develop a sharpness evaluation algorithm on different types of images. The paper reviews, classifies and summarizes the past decade's sharpness evaluation methods for no-reference images. The reviewed evaluation methods are grouped into four categories with their evaluation results compared and the advantages and disadvantages discussed.