The Analysis of Image Enhancement for Target Detection
In the process of automatic detection and recognition based on image, the quality of the detected images affects the target detection and recognition results. To solve the problem of low contrast and high signal-to-noise ratio of the target image in the target detection process, this paper introduces two types of image detail enhancement algorithms which are widely used in recent years, including brightness contrast image enhancement algorithm and HSV color space based enhancement algorithm, and its impact on the target detection. Experiments show that the image detail enhancement can improve the overall and local contrast of the image, highlight the details of the image, and the enhanced image can effectively improve the number and accuracy of the target detection.
KeywordsImage enhancement Brightness contrast HSV color space Target detection
This research work is supported by the grant of Guangxi science and technology development project (No: AC16380124), the grant of Guangxi Science Foundation (No: 2017GXNSFAA198226), the grant of Guangxi Key Laboratory of Trusted Software of Guilin University of Electronic Technology (No: KX201601), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics of Guilin University of Electronic Technology (No: GIIP201602), and the grant of Innovation Project of GUET Graduate Education (2017YJCX55), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics (No. GIIP201602),the grant of Guangxi Key Laboratory of Trusted Software (No. kx201601), Guangxi Cooperative Innovation Center of cloud computing and Big Data, the grant of Guangxi Colleges and Universities Key Laboratory of cloud computing and complex systems (No. YD16E11), the grant of Guangxi Key Laboratory of cryptography and information security (GCIS201601, GCIS201602, GCIS201603).
- 4.Yang, S., Oh, J.H., Park, Y.: Contrast enhancement using histogram equalization with bin underflow and bin overflow. In: Proceedings 2003 International Conference on Image Processing, Spain, pp. 881–884(2003)Google Scholar
- 8.Ein-Shoka, A.A., Kelash, H.M.: Enhancement of IR images using homomorphic filtering in fast discrete curvelet transform(FDCT). J. Int. J. Comput. Appl. 96(8), 22–25 (2014)Google Scholar
- 9.Delac, K., Grgic, M., Kos, T.: Sub-image homomorphic filtering technique for improving facial identification under difficult illumination conditions. In: International Conference on Systems, Signals and Image Processing, Budapest, Hungary, pp. 95–98 (2006)Google Scholar