Research on non-reference text image blur assessment system. (English) Zbl 07929660
Wang, Wei (ed.) et al., Communications, signal processing, and systems. Proceedings of the 12th international conference, September 6–8, 2023. Volume 1. Singapore: Springer. Lect. Notes Electr. Eng. 1032, 467-475 (2024).
Summary: The non-reference image blur assessment (NR-TIBA) system is significant in text image processing. Due to the high cost of subjective evaluation and the unavailability of reference images in the natural environment, an objective NR-TIBA system is essential. Quantitatively, some traditional methods are affected by the richness of image content, while deep learning methods are too expensive to label; qualitatively, research on text image blur evaluation is scarce. This paper proposes a simple evaluation system for quantitative analysis using deep learning qualitative analysis and the combination of the spatial domain and frequency domain. It has good quantitative performance and only requires a small amount of complex labeling data. Experiments show that the proposed system performs well and can be further applied.
For the entire collection see [Zbl 1537.94013].
For the entire collection see [Zbl 1537.94013].
MSC:
| 94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |
| 68U10 | Computing methodologies for image processing |
| 94A12 | Signal theory (characterization, reconstruction, filtering, etc.) |
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