FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-11ngwcn01

Research Article |

Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications

Author(s): Chinthakindi Kiran Kumar* Gaurav Sethi and Kirti Rawal,

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Special Issue on NGWCN

Publisher : FOREX Publication

Published : 30 December 2023

e-ISSN : 2347-470X

Page(s) : 01-09

DOI: https://doi.org/10.37391/ijeer.11ngwcn01

Abstract

The lower image quality and higher noise levels of low light images can have a substantial effect on wireless communication. Images typically have less brightness, less clarity, and more picture noise in low light situations. The precision and dependability of picture-based communication may be impacted by this decline in image quality. Low light levels can be detrimental to video chats since it becomes difficult for the camera to get clear pictures of the user's face. This may lead to decreased video quality, trouble with facial recognition, and a generally worse user experience when communicating over video. Low light illumination correction algorithms are frequently made to work quickly and effectively in order to enable real-time video-based wireless communication. Correcting uneven illumination or low light in images is crucial for various fields; as such images pose challenges for human recognition, computer vision algorithms, and multimedia algorithms. An adaptive illumination correction algorithm based on a simple log transformation approach enhanced by a hyperbolic beta transformation and a logarithmic image processing is presented in this research. It has an adaptive optimization function that adjusts itself based on the image’s illumination and reflectance. Low computation and minimal parameter optimization are the hallmarks of this method. A variety of illuminated images and video sequences from various datasets are used to evaluate the suggested adaptive illumination approach. This adaptive illumination approach's experimental findings are impressive compared to those produced using prior techniques. The results reveal that the proposed system can adapt to various image-based communication and surveillance video sequences taken under various illumination conditions.

Keywords: Illumination correction, exponential function, hyperbolic tangent function, Log Transformation, wireless communications.




Chinthakindi Kiran Kumar*, Lovely Professional University, India; Email: ckkmtech11@gmail.com

Gaurav Sethi, Lovely Professional University, India; Email: gaurav.11106@lpu.co.in

Kirti Rawal, Lovely Professional University, India; Email: kirti.20248@lpu.co.in

    [1] X. Guo, Y. Li and H. Ling.(2017), "LIME: Low-Light Image Enhancement via Illumination Map Estimation," in IEEE Transactions on Image Processing, Vol. 26 No. 2, pp. 982-993.
    [2] Jiang, X., Yao, H. & Liu, D.(2019), “Nighttime image enhancement based on image decomposition” Signal, Image and Video Processing Vol.13 No.1,pp. 189–197.
    [3] H. -H. Lai, Y. -D. Wu and C. -W. Tang, "An Illumination Adaptive Video Coding Scheme for In-Vehicle Video Applications," VTC Spring 2008 - IEEE Vehicular Technology Conference, Marina Bay, Singapore, 2008, pp. 2991-2995, doi: 10.1109/VETECS.2008.318.
    [4] Haoning Lin and Zhenwei Shi.(2014), “Multi-scale retinex improvement for nighttime image enhancement” Optik, Vol. 125 No.24, pp. 7143-7148.
    [5] Ying, Z., Li, G., Ren, Y., et al.(2017), “A new low-light image enhancement algorithm using camera response model”. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 3015–3022.
    [6] X. Ren, M. Li, W. -H. Cheng and J. Liu.(2018) "Joint Enhancement and Denoising Method via Sequential Decomposition," IEEE International Symposium on Circuits and Systems (ISCAS),pp. 1-5.
    [7] Mahmoud Afifi.(2019),“Semantic White Balance: “Semantic Color Constancy Using Convolutional Neural Network”, Computer Vision and Pattern Recognition.
    [8] Wang, M. and Shang, Z. (2019). Deep Separable Convolution Neural Network for Illumination Estimation. Proceedings of the 11th International Conference on Agents and Artificial Intelligence. [online] pp. 879-886.
    [9] Murugan, R.A., Sathyabama, B. Object Detection for Night Surveillance Using Ssan Dataset Based Modified Yolo Algorithm in Wireless Communication. Wireless Pers Commun 128, 1813–1826 (2023). https://doi.org/10.1007/s11277-022-10020-9.
    [10] Y. Hu, Y. Shang, X. Fu and H. Ding,(2015), "A low illumination video enhancement algorithm based on the atmospheric physical model," 8th International Congress on Image and Signal Processing (CISP), Shenyang, pp. 119-124.
    [11] Y. Que, Y. Yang and H. J. Lee.(2019) "Exposure Measurement and Fusion via adaptive Multiscale Edge-Preserving Smoothing," in IEEE Transactions on Instrumentation and Measurement, Vol.68 No.12, pp. 4663-4674.
    [12] Artur Łoza, David R. Bull, Paul R. Hill, Alin M. Achim.(2013),“Automatic contrast enhancement of lowlight images based on local statistics of wavelet coefficients”, Digit. Signal Process., Vol.23 No.6, pp. 1856–1866.
    [13] R.Mantiuk, K.Myszkowski, H.P. Seidel.(2006),“A perceptual framework for contrast processing of high dynamic range images”, ACMTrans.Appl.Percept,Vol.3 No.3, pp.286–308.
    [14] Chen, D., Mirebeau, J.M., Cohen, L.D. (2016),“Finsler geodesic evolution model for region based active contours”. Proc. of the British Machine Vision Conf. (BMVC), York, UK, pp. 22.1–22.12.
    [15] Florea, Corneliu and Florea, Laura.(2013), “Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions”. International Journal of Applied Mathematics and Computer Science.Vol.23 No.3,pp. 637 – 648.
    [16] Constantin Vertan, Alina Oprea, Corneliu Florea, Laura Florea.(2008), “A pseudo-logarithmic framework for edge detection” Advanced Concepts for Intelligent Vision Systems, Vol. 5259, pp. 637–644.
    [17] Patrasincu, V. and Voicu, I.(2000)“An algebraical model for gray level images”, Proceedings of the Exhibition on Optimization of Electrical and Electronic Equipment, OPTIM, Brasov, Romania, pp. 809–812.
    [18] Panetta, K., Zhou, Y., Agaian, S. and Wharton, E.(2011)“Parameterized logarithmic framework for image enhancement”, IEEE Transactions on Systems, Man, and Cybernetics, B: Cybernetics Vol.41, no.2, pp.460–472.
    [19] M. S. Farid, M. Lucenteforte and M. Grangetto.(2015) "Blind depth quality assessment using histogram shape analysis.,"3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), Lisbon, pp. 1-5.
    [20] Al-Ameen, Zohair.(2019), “Nighttime Image Enhancement Using a New Illumination Boost Algorithm”. IET Image Processing. Vol 13, pp. 1314-1320.
    [21] Mittal, A., A. K. Moorthy, and A. C. Bovik.(2012),"No-Reference Image Quality Assessment in the Spatial Domain." IEEE Transactions on Image Processing. Vol. 21 No.12, pp. 4695–4708.
    [22] Fischer, M., Vaughan, D.(2016), “The beta-hyperbolic secant distribution”, Austrian J. Stat., Vol.39 No.3, pp. 245–258.
    [23] Waterloo IVC Multi-Exposure Fusion Image Database. Accessed: Apr. 2018. [Online]. Available: http://ivc.uwaterloo.ca/database/MEF/MEF–Database.php.
    [24] Y. Que, Y. Yang and H. J. Lee.(2019) "Exposure Measurement and Fusion via adaptive Multiscale Edge-Preserving Smoothing," in IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 12, pp. 4663-4674.
    [25] Y. Wang, P.-M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, CDnet 2014: An Expanded Change Detection Benchmark Dataset,in Proc. IEEE Workshop on Change Detection (CDW-2014) at CVPR-2014, pp. 387-394.
    [26] G.J. Brostow “Motion-based Segmentation and Recognition Dataset.” http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/
    [27] Jinxiang Ma,Xinnan Fan,Xuewu Zhang,Xifang Zhu.(2018)"Contrast Limited adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement",International Journal of Pattern Recognition and Artificial Intelligence,Vol. 32, No. 07.
    [28] Athar A.Ein-shoka,Osama S.Faragallah.(2018),"Quality enhancement of infrared images using dynamic fuzzy histogram equalization and high pass adaptation in DWT",Optik,Vol:160, pp: 146-158.
    [29] F. Kou, W. Chen, Z. Li and C. Wen.(2015), "Content adaptive Image Detail Enhancement," in IEEE Signal Processing Letters, vol. 22, no. 2, pp. 211-215.
    [30] Z. Li and J. Zheng.(2018), "Single Image De-Hazing Using Globally Guided Image Filtering," in IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 442-450.
    [31] Gao MZ., Wu ZG., Wang L.(2013), “Comprehensive Evaluation for HE Based Contrast Enhancement Techniques”. In: Pan JS., Yang CN., Lin CC. (eds) Advances in Intelligent Systems and Applications – Vol.2, pp.331-338.
    [32] S. Bae and M. Kim.(2016), "A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception," in IEEE Transactions on Image Processing, Vol. 25 No. 5, pp. 2392-2406.
    [33] Sara, Umme, Morium Akter, and Mohammad Shorif Uddin. "Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study." Journal of Computer and Communications 7.3 (2019): 8-18.
    [34] W. Wang, X. Wu, X. Yuan and Z. Gao.(2020),"An Experiment-Based Review of Low-Light Image Enhancement Methods," in IEEE Access, vol. 8, pp. 87884-87917.

Chinthakindi Kiran Kumar, Gaurav Sethi, and Kirti Rawal (2023), Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications. IJEER 11(ngwcn), 01-09. DOI: 10.37391/ijeer.11ngwcn01.