NEWS

LOG-STAT: AN ILLUMINATION-BALANCING ALGORITHM FOR ARCHAEOLOGICAL IMAGES CAPTURED IN NON-IDEAL LIGHTING


(Received: 18-Oct.-2025, Revised: 6-Dec.-2025 , Accepted: 23-Dec.-2025)
Archaeological images are occasionally captured in environments with non-ideal lighting. This results in imbalanced illumination and a loss of detail. These problems hinder precise operations, such as analysis, interpretation, representation, and 3D modeling. This study introduces a non-complex illumination balancing algorithm called Log-Stat, leveraging logarithmic approaches and statistical methods. It also includes two main phases, one for illumination balancing and the other for tonality adjustment. The first phase utilizes six mathematical equations, while the second phase utilizes four equations, processing the V channel of the image in the HSV color model. Various images have been used to test the algorithm, and a comparison with ten prominent algorithms is achieved, evaluating the outcomes using six measures. The results have shown the success of Log- Stat in different aspects, including fidelity recovery and illumination balance. This allowed better visualization of details, which the unbalanced illumination effect hindered. Integrating appropriate methods and fine-tuning the parameters enabled the Log-Stat to perform in dissimilar illumination situations.

[1] C. Morgan and H. Wright, "Pencils and Pixels: Drawing and Digital Media in Archaeological Field Recording," Journal of Field Archaeology, vol. 43, no. 2, pp. 136–151, 2018.

[2] O. A. Basheer and Z. Al-Ameen, "Illumination Enhancement of Nighttime Images Using a Regulated Single Scale Retinex Algorithm," Jordanian Journal of Computers and Information Technology (JJCIT), vol. 10, no. 2, pp. 138–151, 2024.

[3] P. Sapirstein and S. Murray, "Establishing Best Practices for Photogrammetric Recording during Archaeological Fieldwork," Journal of Field Archaeology, vol. 42, no. 4, pp. 337–350, 2017.

[4] H. Kaur and N. Sohi, "A Novel Enhancement Method for Colored Rock Art Archaeological Images," Int. J. Adv. Res. Comput. Sci. (IJARCS), vol. 8, no. 7, pp. 1163–1167, 2017.

[5] S. Sylaiou et al., "Redefining Archaeological Research: Digital Tools, Challenges and Integration in Advancing Methods," Applied Sciences, vol. 15, no. 5, p. 2495, 2025.

[6] L. Marchesotti, N. Murray and F. Perronnin, "Discovering Beautiful Attributes for Aesthetic Image Analysis," Int. J. of Computer Vision (IJCV), vol. 113, pp. 246–266, 2015.

[7] M. G. Robinson, Photogrammetry for Archaeological Objects: A Manual, ISBN-10, 1743329830, Sydney, Australia: Sydney Univ. Press, 2024.

[8] S. Kang et al., "Image Intrinsic Components Guided Conditional Diffusion Model for Low-light Image Enhancement," IEEE Trans. Circuits Syst. Video Technol., vol. 34, no. 12, pp. 13244–13256, 2024.

[9] S. Xu, X. Chen, B. Song, C. Huang and J. Zhou, "CNN Injected Transformer for Image Exposure Correction," Neurocomputing, vol. 587, p. 127688, 2024.

[10] N. Singhal, A. Kadam, P. Kumar, H. Singh and A. Thakur, "Study of Recent Image Restoration Techniques: A Comprehensive Survey," Jordanian Journal of Computers and Information Technology (JJCIT), vol. 11, no. 2, pp. 211–237, 2025.

[11] X. Fu et al., "A Probabilistic Method for Image Enhancement with Simultaneous Illumination and Reflectance Estimation," IEEE Trans. Image Process., vol. 24, no. 12, pp. 4965–4977, 2015.

[12] X. Fu et al., "A Fusion-based Enhancing Method for Weakly Illuminated Images," Signal Process., vol. 129, pp. 82–96, 2016.

[13] X. Guo, Y. Li and H. Ling, "LIME: Low-light Image Enhancement via Illumination Map Estimation," IEEE Trans. Image Process., vol. 26, no. 2, pp. 982–993, 2017.

[14] Y. Ren, Z. Ying, T. H. Li and G. Li, "LECARM: Low-light Image Enhancement Using the Camera Response Model," IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 4, pp. 968–981, 2018.

[15] M. Tanaka, T. Shibata and M. Okutomi, "Gradient-based Low-light Image Enhancement," Proc. of the 2019 IEEE Int. Conf. on Consumer Electronics (ICCE), DOI: 10.1109/ICCE.2019.8662059, Las Vegas, NV, USA, Jan. 2019.

[16] J. Xie et al., "Semantically-guided Low-light Image Enhancement," Pattern Recognition Letters, vol. 138, pp. 308–314, 2020.

[17] N. Singh and A. K. Bhandari, "Principal Component Analysis-based Low-light Image Enhancement Using Reflection Model," IEEE Trans. Instrum. Meas., vol. 70, pp. 1–10, 2021.

[18] J. J. Jeon and I. K. Eom, "Low-light Image Enhancement Using Inverted Image Normalized by Atmospheric Light," Signal Process., vol. 196, p. 108523, 2022.

[19] Y. Demir and N. H. Kaplan, "Low-light Image Enhancement Based on Sharpening-Smoothing Image Filter," Digital Signal Processing, vol. 138, p. 104054, 2023.

[20] M. F. Hassan, T. Adam, H. Rajagopal and R. Paramesran, "A Hue Preserving Uniform Illumination Image Enhancement via Triangle Similarity Criterion in HSI Color Space," Visual Computer, vol. 39, no. 12, pp. 6755–6766, 2023.

[21] L. Wang, L. Zhao, T. Zhong and C. Wu, "Low-light Image Enhancement Using Generative Adversarial Networks," Scientific Reports, vol. 14, no. 1, p. 18489, 2024.

[22] I. M. Majid Mohammed and N. A. Mat Isa, "Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image Enhancement," IEEE Access, vol. 13, pp. 62600–62632, 2025.

[23] S. Yang, D. Zhou, J. Cao and Y. Guo, "LightingNet: An Integrated Learning Method for Low-light Image Enhancement," IEEE Trans. Comput. Imaging, vol. 9, pp. 29–42, 2023.

[24] C. Zhang, K. M. Lam and Q. Wang, "CoF-Net: A Progressive Coarse-to-fine Framework for Object Detection in Remote-sensing Imagery," IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–17, 2023.

[25] S. J. Im, C. Yun, S. J. Lee and K. R. Park, "Artificial Intelligence-based Low-light Marine Image Enhancement for Semantic Segmentation in Edge-intelligence-empowered Internet of Things Environment," IEEE Internet Things J., vol. 12, no. 4, pp. 4086–4114, 2025.

[26] C. Li, J. Guo, F. Porikli and Y. Pang, "LightenNet: A Convolutional Neural Network for Weakly Illuminated Image Enhancement," Pattern Recognition Letters, vol. 104, pp. 15–22, 2018.

[27] M. Afifi et al., "CIE XYZ Net: Unprocessing Images for Low-level Computer Vision Tasks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 9, pp. 4688–4700, 2022.

[28] Y. Cai, H. Bian, J. Lin, H. Wang, R. Timofte and Y. Zhang, "Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement," Proc. of the IEEE/CVF Int. Conf. on Computer Vision (ICCV), pp. 12504–12513, 2023.

[29] Y. Cui, W. Ren, X. Cao and A. Knoll, "Revitalizing Convolutional Network for Image Restoration," IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 12, pp. 9423–9438, 2024.

[30] L. Xu, C. Hu, Y. Hu, X. Jing, Z. Cai and X. Lu, "UPT-Flow: Multi-scale Transformer-guided Normalizing Flow for Low-light Image Enhancement," Pattern Recognition, vol. 158, p. 111076, 2025.

[31] S. Bansal, R. K. Bansal and R. Bhardwaj, "A Novel Low Complexity Retinex-based Algorithm for Enhancing Low-light images," Multimedia Tools Appl., vol. 83, no. 10, pp. 29485–29504, 2024.

[32] A. Łoza et al., "Automatic Contrast Enhancement of Low-light Images Based on Local Statistics of Wavelet Coefficients," Digital Signal Processing, vol. 23, no. 6, pp. 1856–1866, 2013.

[33] M. Jourlin and J. C. Pinoli, "A Model for Logarithmic Image Processing," Journal of Microscopy, vol. 149, no. 1, pp. 21–35, 1988.

[34] X. Pei et al., "Robustness of Machine Learning to Color, Size Change, Normalization and Image Enhancement on Micrograph Datasets with Large Sample Differences," Materials & Design, vol. 232, p. 112086, 2023.

[35] O. Bryan et al., "A Diffusion-based Super Resolution Model for Enhancing Sonar Images," Journal of the Acoustical Society of America, vol. 157, no. 1, pp. 509–518, 2025.

[36] M. Ambrosanio, B. Kanoun and F. Baselice, "WKSR-NLM: An Ultrasound Despeckling Filter Based on Patch Ratio and Statistical Similarity," IEEE Access, vol. 8, pp. 150773–150783, 2020.

[37] S. Wang, J. Zheng, H. M. Hu and B. Li, "Naturalness Preserved Enhancement Algorithm for Non-uniform Illumination images," IEEE Trans. Image Process., vol. 22, no. 9, pp. 3538–3548, 2013.

[38] X. Min, G. Zhai, K. Gu, Y. Liu and X. Yang, "Blind Image Quality Estimation via Distortion Aggravation," IEEE Trans. on Broadcasting, vol. 64, no. 2, pp. 508–517, 2018.

[39] C. Gao, K. Panetta and S. Agaian, "Color Image Attribute and Quality Measurements," Proc. SPIE Mobile Multimedia/Image Processing, Security and Applications, vol. 9120, pp. 238–251, May 2014.

[40] N. Venkatanath, D. Praneeth, S. C. Sumohana and S. M. Swarup, "Blind Image Quality Evaluation Using Perception-based Features," Proc. of the 2015 21st National Conf. on Communications (NCC), pp. 1–6, Mumbai, India, 2015.

[41] W. Xue et al., "Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features," IEEE Trans. Image Process., vol. 23, no. 11, pp. 4850–4862, 2014.

[42] N. Singh and A. K. Bhandari, "Noise Aware L₂–LP Decomposition-based Enhancement in Extremely Low Light Conditions with Web Application," IEEE Trans. on Consumer Electronics, vol. 68, no. 2, pp. 161–169, 2022.