Hybrid Domain Analysis of Noise-Aided Contrast Enhancement Using Stochastic Resonance
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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.
KeywordsContrast enhancement Dynamic stochastic resonance Hybrid domain SVD-DWT DCT-DWT SVD-DCT
The authors would like to thank Mr. Sajan Pillai, for providing the test images used in the study. The authors are also grateful to the members of the Image Processing and Computer Vision Lab of IIT Kharagpur, and various other volunteers for their valuable participation in the subjective evaluation study.
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