Hybrid Domain Analysis of Noise-Aided Contrast Enhancement Using Stochastic Resonance

This paper aims to present an analysis of a noise-aided contrast enhancement algorithm in hybrid transform domains. The performance of our earlier noise-enhanced iterative algorithm, formulated from the motion dynamics of a double-well system exhibiting dynamic stochastic resonance, has been investigated here on hybrid coefficients, viz. singular values (SVs) of wavelet coefficients, SVs of discrete cosine transform (DCT) coefficients, and DCT of wavelet coefficients, of a dark image. The performance of the algorithm is gauged using metrics indicating relative contrast enhancement and perceptual quality. Colorfulness, subjective visual scores and logarithmic contrast metrics for outputs are also observed. Experimental results display noteworthy enhancement of contrast on both natural and synthetically-darkened images. It can be inferred from comparative analysis with respect to other conventional methods that while the algorithm is observed to work well in all three hybrid domains, the SV-DCT domain performs better in terms of iteration count, while DCT-DWT is found to outperform others in terms of perceptual quality.

[1]  Gholamreza Anbarjafari,et al.  Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition , 2010, IEEE Geoscience and Remote Sensing Letters.

[2]  Hao Chen,et al.  Stochastic resonance: An approach for enhanced medical image processing , 2007, 2007 IEEE/NIH Life Science Systems and Applications Workshop.

[3]  Prabir Kumar Biswas,et al.  Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance , 2012, ICVGIP '12.

[4]  Paul S. Heckbert,et al.  Graphics gems IV , 1994 .

[5]  Joseph W. Goodman,et al.  A mathematical analysis of the DCT coefficient distributions for images , 2000, IEEE Trans. Image Process..

[6]  Wiesenfeld,et al.  Theory of stochastic resonance. , 1989, Physical review. A, General physics.

[7]  G. Parisi,et al.  Stochastic resonance in climatic change , 1982 .

[8]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[9]  Kiyoharu Aizawa,et al.  Internal noise-induced contrast enhancement of dark images , 2012, 2012 19th IEEE International Conference on Image Processing.

[10]  S. Acton,et al.  Image enhancement using a contrast measure in the compressed domain , 2003, IEEE Signal Processing Letters.

[11]  Hakil Kim,et al.  Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance , 2011, Pattern Recognit. Lett..

[12]  Haining Huang,et al.  A SR-based radon transform to extract weak lines from noise images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[13]  Rajib Kumar Jha,et al.  Noise-induced contrast enhancement using stochastic resonance on singular values , 2014, Signal Image Video Process..

[14]  Zhou Wang,et al.  No-reference perceptual quality assessment of JPEG compressed images , 2002, Proceedings. International Conference on Image Processing.

[15]  Prabir Kumar Biswas,et al.  Enhancement of dark and low-contrast images using dynamic stochastic resonance , 2013, IET Image Process..

[16]  Sos S. Agaian,et al.  Transform-based image enhancement algorithms with performance measure , 2001, IEEE Trans. Image Process..

[17]  Gregoire Nicolis,et al.  Stochastic resonance , 2007, Scholarpedia.

[18]  Chunhua Zhang,et al.  Image enhancement using stochastic resonance [sonar image processing applications] , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[19]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[20]  Okan K. Ersoy,et al.  Transform image enhancement , 1992, Optical Society of America Annual Meeting.

[21]  Anil Kumar,et al.  Enhancement of Low Contrast Satellite Images using Discrete Cosine Transform and Singular Value Decomposition , 2011 .

[22]  R. K. Jha,et al.  Noise-enhanced contrast stretching of dark images in SVD-DWT domain , 2013, 2013 Annual IEEE India Conference (INDICON).

[23]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[24]  H. Risken.  The Fokker-Planck equation : methods of solution and applications , 1985 .

[25]  Prabir Kumar Biswas,et al.  Image enhancement and dynamic range compression using novel intensity-specific stochastic resonance-based parametric image enhancement model , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[26]  Sanjit K. Mitra,et al.  Enhancement of Color Images by Scaling the DCT Coefficients , 2008, IEEE Transactions on Image Processing.

[27]  Max-Olivier Hongler,et al.  The Resonant Retina: Exploiting Vibration Noise to Optimally Detect Edges in an Image , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Prasun Kumar Roy,et al.  Magnetic resonance image enhancement using stochastic resonance in Fourier domain. , 2010, Magnetic resonance imaging.

[29]  R. K. Jha,et al.  Noise-induced contrast enhancement of dark images using non-dynamic stochastic resonance , 2012, 2012 National Conference on Communications (NCC).

[30]  Ching-Chung Yang,et al.  Image enhancement by the modified high-pass filtering approach , 2009 .

[31]  Stefan Winkler,et al.  Color image quality on the Internet , 2003, IS&T/SPIE Electronic Imaging.

[32]  Sos S. Agaian,et al.  Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy , 2007, IEEE Transactions on Image Processing.

[33]  V. P. Subramanyam Rallabandi,et al.  Enhancement of ultrasound images using stochastic resonance-based wavelet transform , 2008, Comput. Medical Imaging Graph..