# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Combination Methods  
Concluded from the above works of literature, different evaluation methods have different characteristics. The combination evaluation methods combine two or more methods to give full play to their respective advantages. For example, combining spatial domain methods with deep learning methods can improve the accuracy of evaluation results based on the integrity of the extracted features. The combined framework of combination methods is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Machine-based methods} & Good performance on small & Evaluation results depend on \\  & sample training set & feature extraction. \\ \hline \multirow{2}{*}{Deep learning-based methods} & Automatically train learning & \multirow{2}{*}{A large amount of data} \\  & features from a large number & \\  & of samples & \\ \hline \multirow{2}{*}{Dictionary learning-based methods} & Advanced features of samples & \multirow{2}{*}{The evaluation effect depends} \\  & can be extracted. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Advantages and disadvantages of different learning-based methods.  
[PERSON] et al. [88] utilized both spectral and spatial properties of the image to quantify the overall perceived sharpness of the entire image by combining the slope of the magnitude spectrum and the total spatial variation with a weighted geometric average. [PERSON] et al. [89] combined the spatial domain characteristics and the clarity of ResNet of the images to evaluate the clarity of the components to maintain the reliability, security of power transmission by observing the images. [PERSON] et al. [90] proposed a sharpness assessment method that combines geometric distortion and scale invariance by analyzing the local similarity of images and the similarity between their neighboring regions. The effectiveness of this method is better than other competing methods, but the disadvantage is that it is time-consuming. [PERSON] et al. [91] obtained spatial and shape information by calculating grayscale and gradient maps, obtained salient maps by using scale invariant feature transform (SIFT) and then generated fuzzy evaluation scores by discrete cosine transform (DCT). The results demonstrated that the fuzzy scores generated by the proposed method were highly correlated with subjective ratings. [PERSON] et al. [92] presented a new image structure change model for image quality assessment, which uses fuzzy logic to classify and score each pixel's structure change in the distorted image. [PERSON] et al. [93] proposed a semantic feature aggregation (SFA)-based evaluation method to mitigate the effect of complex image content on the evaluation results. This method extracts features using a trained DCNN model and maps global features to image quality scores. [PERSON] et al. [94] proposed a general no-reference quality assessment framework based on shearlet transform and deep neural networks. The coefficient amplitude is extracted from the sum of multi-scale directional transform (shearlet transform) and sub-band as the main feature to describe the behavior of natural images; then the softmax classifier is used to identify the differences of evolutionary features; finally, the evaluation results are obtained. The experimental results show the excellent performance of the method.  
The combination method combines the advantages of the single methods included, and the evaluation result has a high accuracy rate, which is consistent with the subjective evaluation result. However, it will increase the computational complexity. Currently, scholars are also studying more advanced methods in various aspects.