Name
Abstract | ||
|---|---|---|
Sharpness is an important indicator to evaluate image quality or to optimize parameters in computer vision tasks, such as image acquisition, compression, and restoration. We utilize difference quotients to construct an absolute difference quotient and a relative difference quotient to evaluate the sharpness among images containing difference contents and the sharpness among pixels in the same image, respectively. Based on the constructed quotients, we estimate the pixel sharpness index and the image block sharpness index and create a single sharpness index as the overall sharpness of an image by pooling strategy. Our quotient-based methods can assess image sharpness effectively and efficiently. Experimental results on four simulated databases with real blurring and synthetic blurring images show the proposed sharpness metric is consistent with subjective sharpness evaluations and is competitive with existing sharpness metrics. It achieves a balance between running time and high performance. (C) 2019 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/1.JEI.28.1.013032 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
sharpness assessment,blurriness assessment,difference quotients,image quality | Computer vision,Difference quotient,Pattern recognition,Computer science,Reference image,Artificial intelligence | Journal |
Volume | Issue | ISSN |
28 | 1 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiye Qian | 1 | 43 | 5.60 |
| Hengjun Zhao | 2 | 0 | 0.34 |
| Jin Fu | 3 | 7 | 1.79 |
| Wei Song | 4 | 113 | 15.51 |
| Jide Qian | 5 | 3 | 0.78 |
| Qianbo Xiao | 6 | 0 | 0.34 |