# Review: A Survey on Objective Evaluation of Image Sharpness
## 4 Conclusions and Outlook  
Through the evolution of no-reference image sharpness assessment research, from traditional methods (spatial or spectral domain-based) to learning-based methods to methods combination, this technique has witnessed rapid development in itself. The following conclusions can be raised by analyzing a large number of works of literature.  
(1) Each group of evaluation methods is inseparable from the feature extraction process. The spatial domain-based feature extraction is simple and efficient, which is beneficial for real-time applications, but easily interfered by image noise. The spectral domain feature extraction can effectively remove noise but at the cost of increased time complexity. Reported research reveals that the reasonable selection of the combination in spatial and spectral domains helps improve the accuracy in image quality evaluation [101; 102]. In the learning-based method, the machine learning-based method mainly extracts features manually. With the wide application of deep learning methods, CNN-based feature extraction has become a popular trend for scholars.  
(2) In recent years, methods based on deep learning have achieved good results in evaluating no-reference image clarity. However, due to the complexity of the deep learning network model, a large amount of training data is often required. Actually, it is quite difficult to collect enough real-world images for network training, making it urgent to design a deep learning evaluation method suitable for small-scale datasets.  
In the future research, the sharpness evaluation method combining multiple methods will gradually become a popular trend. We believe that the overall evaluation performance can be further improved by using the deep learning network model to extract features, combined with the high real-time performance in the image space domain and interference elimination in the spectral domain. To summarize, image quality evaluation research has the potential for further development of new research ideas on the ways in practical application in real-world image datasets and taking full advantage of combined methods.  
Conceptualization, methodology, writing--original draft preparation, data curation, M.Z.; methodology, formal analysis, writing--review and editing, project administration, L.Y.; investigation, Z.W.; resources, Z.K.; funding acquisition, supervision, project administration, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 62201441 and 51903199), Key Research and Development Program of Shaanxi (Program No. 2023-YBG7-490), Natural Science Basic Research Program of Shaanxi (Program No. 2019Q-182, 2018 JQ5214, and 2021 JQ-691), Innovation Capability Support Program of Shaanxi (Program No. 2022 KJX-40), Outstanding Young Talents Support Plan of Shaanxi Universities (2020), Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 22 JK0394), Science and Technology Guiding Project of China National Textile and Apparel Council (No.22 JK0394), and No.2020046), and Innovation Capacity Support Plan of Shaanxi, China (No. 2020 PT-043).  
Not applicable.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62201441 and 51903199), Key Research and Development Program of Shaanxi (Program No. 2023-YBG7-490), Natural Science Basic Research Program of Shaanxi (No. 2019Q-182, 2018 JQ5214, and 2021 JQ-691), Innovation Capability Support Program of Shaanxi (Program No. 2022 KJX-40), Outstanding Young Talents Support Plan of Shaanxi Universities (2020), Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 22 JK0394), Science and Technology Guiding Project of China National Textile and Apparel Council (No.2020044 and No.2020046), and Innovation Capacity Support Plan of Shaanxi, China (No. 2020 PT-043).  
The authors declare no potential conflict of interest.