Paper ID:
IJSDIP_2026_VOLUME 14_ISSUE 1_22-33
Author's Name:
B Shoba Rani, Seetharam Khetavath
Volume:
14
Issue:
Issue 1
Year:
2026
Pages:
22-33
Abstract
The enhancement of low-light and low-contrast images is a major problem in the computer vision applications of night-time surveillance, medical imaging as well as remote sensing. It is common with most conventional histogram equalization methods because they do not exhibit adaptation to non-uniform illumination, over-enhancing data, cartoning structural information, and distorting the brightness. In a bid to overcome these challenges, the following paper will present a series of theories called Advanced Recursive Energy Curve-Based Histogram Equalization (AREC-HE) framework, which combines energy curve modeling, adaptive exposure thresholding and recursive sub-image enhancement utilizing edge-preserving filtering. The energy curve gives a spatially sensitive interpretation of luminance changes, thus providing the ability to select an adaptive threshold and control luminance in individual regions, without displacing natural luminance. Quantitative tests performed on a wide range of low-light image datasets reveal the fact that AREC-HE performs much better than classical HE, BBHE, CLAHE, DSIHE, ESIHE, and RS-ESIHE-E, with 4-5 dB higher PSNR, higher SSIM and FSIM, and higher entropy, and lower AMBE. The suggested approach is successful at improving fine details, edge structure and color preservation, which makes it appropriate to human visual perception as well as automated vision systems.
References
[1] S. Singh, M. Sahil, B. Kumar and H. Singh, "Performance Evaluation of Various Histogram Equalization Techniques on Thermal Images," 2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI), Gwalior, India, 2023, pp. 1-6, doi: 10.1109/CVMI59935.2023.10464646.
[2] M. F. Fadillah, R. R. Nurmalasari, L. Kamelia and Mursidin, "The Combination of Contrast Stretching and Histogram Equalization Methods to Optimizing Radiology Image Quality," 2023 9th International Conference on Wireless and Telematics (ICWT), Solo, Indonesia, 2023, pp. 1-5, doi: 10.1109/ICWT58823.2023.10335190.
[3] N. V. Bansode, V. Ingale and S. Ingale, "An Appropriate Histogram Equalization Technique for Thermal Image Enhancement of Neck Region," 2023 IEEE Pune Section International Conference (PuneCon), Pune, India, 2023, pp. 1-5, doi: 10.1109/PuneCon58714.2023.10449989.
[4] C. S. Rajput, S. Sharma and R. Mishra, "Simplified Implementation of Histogram Equalization Using Approximation Offset," 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), Greater Noida, India, 2025, pp. 170-177, doi: 10.1109/IC3ECSBHI63591.2025.10991276.
[5] M. Abdullah-Al-Wadud et al., “A dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consumer Electronics, 2007.
[6] S. Arici et al., “A histogram modification framework and its application for image contrast enhancement,” IEEE TIP, 2009.
[7] Q. Wang, R.K. Ward, “Fast image/video contrast enhancement based on weighted thresholded histogram equalization,” IEEE TCE, 2011.
[8] K. Zuiderveld, “Contrast Limited Adaptive Histogram Equalization,” in Graphics Gems IV, 1994.
[9] T. Celik, T. Tjahjadi, “Contextual and variational contrast enhancement,” IEEE TIP, 2011.
[10] J. Canny, “A computational approach to edge detection,” IEEE TPAMI, 1986.
[11] K. Singh, R. Kapoor, “Image enhancement using exposure-based sub image histogram equalization,” Pattern Recognit. Lett., 2014.
[12] K. Singh, R. Kapoor, “Exposure-based sub image histogram equalization,” Image Vis. Comput., 2015.
[13] B. Shoba Rani, S. Khetavath, “Modified Effective Histogram Equalization Method for Night Time Color Image Enhancement with Energy Curve,” JISEM, 2025
[14] B. Shoba Rani, S. Khetavath, “Low-Contrast Color Image Enhancement with Improvised Histogram Equalization based on Spatial Contextual Information,” Acta Sci., 2025
[15] Z. Wang et al., “Image quality assessment: From error visibility to structural similarity,” IEEE TIP, 2004.
[16] L. Zhang et al., “FSIM: A feature similarity index for image quality assessment,” IEEE TIP, 2011.
[17].N.Domala, R Surender Reddy, Aruna “A Novel Full Reference-Image Quality Assessment (FR-IQA) for Adaptive Visual Perception Improvement”, 6th ICICT, 26 February 2021. DOI: 10.1109/ICICT50816.2021.9358610
[18] A. Hore, D. Ziou, “Image quality metrics: PSNR vs. SSIM,” Proc. ICPR, 2010.
[19] D.M. Chandler, “Seven challenges in image quality assessment,” EURASIP J. Image Video Process., 2013.
[20] X. Fu et al., “A weighted variational model for simultaneous reflectance and illumination estimation,” CVPR, 2016.
[21] Y. Guo et al., “LIME: Low-light image enhancement via illumination map estimation,” IEEE TIP, 2017.
