Науковий вісник НГУ

Combinational image enhancement method based on wavelet domain

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Information technologies, systems analysis and administration

Last Updated on 08 February 2016

Published on 08 February 2016

Hits: 4104

Authors:

Defa Hu, Hunan University of Commerce, Changsha, Hunan, China

Zhuang Wu, Capital University of Economics and Business, Beijing, China

Abstract:

Purpose. The image enhancement technique is to highlight the interesting features or suppress the unnecessary features of the image, and it is a basic image processing technique. The article presents a series of study about how to enhance degra-ded image and get desirable effect. The combinational image enhancement measures based on wavelet domain have been developed, which are very helpful for image enhancement, improve the degraded image contrast greatly, and effectively enhance the overall image quality.

Methodology. The high-frequency and low-frequency components in the original image were separated by wavelet decomposition. Different methods were employed to enhance the image detail components of different frequency scopes and highlight the details of different scales. The low-frequency and high-frequency components were combined through the wavelet reconstruction to obtain the final enhanced image so as to improve the visual effect of the image.

Findings. A new wavelet domain algorithm was proposed for image enhancement. It adjusts the lightness of the image, expands the dynamic grayscale scope of the image, and enhances the contrast. The method carries out the self-adaptive enhancement of an image by some appropriate correction in the degraded image clarity.

Originality. The image enhancement method based on wavelet domain has been analysed sistematically. The traditional wavelet analysis method was improved according to the different collected information loss level of the degraded image.

Practical value. The research results greatly enhance the details of the image, improve the overall clarity; and the best effect is achieved when the algorithm is used to improve degraded images. The algorithm can improve the degraded image contrast greatly, effectively enhance the overall image quality without losing the image information during the processing. 

References:

1. João Miguel Pires Dias, Carlos Manta Oliveira and Luís A. da Silva Cruz (2014), “Retinal image quality assessment using generic image quality indicators”, Information Fusion, vol.19, no.9, pp. 73−90.

2. Mingwei Sheng, Yongjie Pang, Lei Wan (2014), “Underwater images enhancement using MultiWavelet transform and median filter”, TELKOMNIKA Indonesian Journal of Electrical Engineering, vol.12, no.3, pp. 2306−2313.

3. Artur Łoza, David R. Bull, Paul R. Hill and Alin M. Achim. (2013), “Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients”, Digital Signal Processing, vol.23, no.6, pp. 1856−1866.

4. Bhandari, A.K., Soni, V., Kumar, A. and Singh, G.K. (2014), “Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT–SVD”, ISA Trans-actions, vol.53, no.4, pp. 1286−1296.

5. Lei Wang, Nian-de Jiang and Xing Ning (2012), “Research on medical image enhancement algorithm based on GSM model for wavelet coefficients”, Physics Procedia, vol. 33 no. 6, pp. 1298−1303.

6. Alex F. de Araujo, Christos E. Constantinou and João Manu-el R.S. Tavares (2014), “New artificial life model for image enhancement”, Expert Systems with Applications, vol. 41, no.13, pp. 5892−5906.

7. Claudia Defrasne (2014), “Digital image enhancement for recording rupestrian engravings: Applications to an alpine rockshelter”, Journal of Archaeological Science, vol.50, no.10, pp. 31−38.

8. Muhammad Zafar Iqbal, Abdul Ghafoor, Adil Masood Siddiqui, Muhammad Mohsin Riaz and Umar Khalid. (2014), “Dual-tree complex wavelet transform and SVD based medical image resolution enhancement”, Signal Processing, vol.105, no.12, pp. 430−437.

9. Mohammad Reza Yousefi, Reza Jafari, Hamid Abrishami Moghaddam. (2014), “Imposing boundary and interface conditions in multi-resolution wavelet Galerkin method for numerical solution of Helmholtz problems”, Computer Methods in Applied Mechanics and Engineering, vol.276, no.1, pp. 67−94.

10. Andò, B., Baglio, S. and Pistorio, A. (2014), “A low cost multi-sensor strategy for early warning in structural monitoring exploiting a wavelet multiresolution paradigm”, Procedia Engineering, vol.87, no. 8, pp. 1282−1285.

2015_06_hu
2016-02-08  815.73 KB  1064

• < Prev