doi: 10.7763/IJMLC.2012.V2.220
Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain
Rajlaxmi Chouhan1
,
C. Pradeep Kumar2,3 ,
Rawnak Kumar2,4 ,
Rajib Kumar Jha2
- 1Department of Electronics and Electrical Communication Engineering, Indian Institute of Information Technology Kharagpur, India
- 2Indian Institute of Information Technology, Design and Manufacturing Jabalpur, India
- 3Infosys Ltd. Kurnool, India
- 4Symbiosis Centre for Management and Human Resource Development, Pune, India
Abstract
In this paper, a dynamic stochastic resonance (DSR)-based technique in discrete wavelet transform (DWT) domain is presented for the enhancement of very dark grayscale and colored images. Generally in DSR, the performance of an input signal can be improved by addition of external noise. However in this paper, the intrinsic noise of an image has been utilized for the purpose of contrast enhancement. The DSR procedure iteratively tunes the DWT coefficients using bistable system parameters. The DSR-based technique significantly enhances the image without introducing any blocking, ringing or spot artifacts. The algorithm has been optimized and made adaptive. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to-background enhancement measure based on standard deviation (TBEs) and target-to-background enhancement measure based on entropy (TBEe). When compared with the existing enhancement techniques such as histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering and Fourier-based DSR, the DWT-based DSR technique gives better performance in terms of visual information, color preservation and computational complexity of the enhancement process.
Keywords
- Metabolic networks
- modeling
- visualization
How to Cite
Rajlaxmi Chouhan, C. Pradeep Kumar, Rawnak Kumar, and Rajib Kumar Jha, "Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain," International Journal of Machine Learning, vol. 2, no. 5, pp. 711-715, 2012. https://doi.org/10.7763/IJMLC.2012.V2.220
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).