Unified Model for Contrast Enhancement and Denoising

Alex Pappachen James, Olga Krestinskaya, Joshin John Mathew

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


In this paper, we attempt a challenging task to unify two important complementary operations, i.e. contrast enhancement and denoising, that is required in most image processing applications. The proposed method is implemented using practical analog circuit configurations that can lead to near real-time processing capabilities useful to be integrated with vision sensors. Metrics used for performance includes estimation of Residual Noise Level (RNL), Structural Similarity Index Measure (SSIM), Output-to-Input Contrast Ratio (CRo-i), and its combined score (SCD). The class of contrast stretching methods has resulted in higher noise levels (RNL ≥ 7) along with increased contrast measures (CRo-i ≥ eight times than that of the input image) and SSIM ≤ 0.52. Denoising methods generates images with lesser noise levels (RNL ≤ 0.2308), poor contrast enhancements (CRo-i ≤ 1.31) and with best structural similarity (SSIM ≥ 0.85). In contrast, the proposed model offers best contrast stretching (CRo-i = 5.83), least noise (RNL = 0.02), a descent structural similarity (SSIM = 0.6453) and the highest combined score (SCD = 169).
Original languageEnglish (US)
Title of host publicationProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
PublisherIEEE Computer Societyhelp@computer.org
Number of pages6
ISBN (Print)9781509067626
StatePublished - Jul 20 2017
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23


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