Abstract
When an image is captured in low-light, it gets the low visibility. To overcome the low visibility of the image, some operations are to be performed. But in this paper, image enhancement is introduced using illumination mapping. First, R, G, B maximum values in each pixel of the considered image are to be calculated and then convert it into a grey scale image by applying the formulae. Some filters are used to remove the noise, the choice of filter depends on the type of noise, and then the image is preprocessed. The logarithmic transformation helps to increase the brightness and contrast of the image with a certain amount. Earlier there were some methods to enhance the low-light image, but illumination map existence is chosen. In this illumination, the image will be enhanced with the good quality and efficiency. The illumination technique will be the more efficient and more quality. The illumination corrects the R, G, B values to get the desired image, then Gamma Correction is applied. The Gamma Correction is a non-linear power transform, it helps to increase or decrease the brightness of the desired image when a low value of gamma is taken, the brightness will be increased and when a high value of gamma is taken, and the brightness will be decreased. The proposed system is implemented using MATLAB software. When different types of images are applied, different contrast and brightness levels that depend on the type of image are observed.
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References
Ghitta O, Ilea DE, Whelan PF (2013) Texture enhanced histogram equalization using TV-L1 ımage decomposition. IEEE Trans Image Process 22(8):3133–3144
Celik T, Tjahjadi T (2011) Contextual and Variational Contrast Enhancement. IEEE Trans Image Process 20(12):3431–3441
Luo Y, Guan Y-P (2015) Structuralcompensation enhancement method fornonuniform illumination images. Appl Opt 54(10):2929–2938
Guo X, Li Y, Ling H (2017) LIME: Low-Light Image Enhancement via Illumination Map Estimation. IEEE Trans Image Process 26(2):982–993
Low pass filters, https://www.picosecond.com/objects/AN
Yang J, Zhong W, Miao Z (2016) On the Image enhancement histogram processing. In: 3rd ınternational conference on ınformative and cybernetics for computational social systems (ICCSS). Jinzhou, China, pp 252–255
Kubinger W, Vincze M, Ayromiou M (1998) The role of gamma correction in colour image processing. In: 9th European signal processing conference (EUSIPCO 1998). Vienna, Austria, pp 1–4
Noise reduction filters for image processing, https://www.sciencedirect.com/science/article/pii/S1875389212005494
Filters for noise reduction, http://www.radiomuseum.org/forumdata/users/4767/file/Tektronix_VerticalAmplifierCircuits_Part1.pdf
Huang T, Yang G, Tang G (2014) A fast two-dimensional median filtering algorithm. IEEE Trans Acoust Speech Signal Process 27(1):13–18
Gehler P, Rother C, Kiefel M, Zhang L, Scholkopf B (2011) Recovering intrinsic images with a global sparsity prior on reflectance. In: Neural ınformation processing systems. California, United States, pp 765–773
Enhancement methods in image processing, https://in.mathworks.com/discovery/image-enhancement.html
Analysis of image enhancement, http://acharya.ac.in/aigs/firstissuepapers/paper7.pdf
Image pre-processing, https://www.slideshare.net/ASHI14march/image-pre-processing
Image transformations, https://www.tutorialspoint.com/dip/image_transformations.htm
Top-hat transform, https://en.wikipedia.org/wiki/Top-hat_transform
Image processing algorithms part 6: gamma correction, http://www.dfstudios.co.uk/articles/programming/image-programming-algorithms/image-processing-algorithms-part-6-gamma-correction/
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Vijay, V., Siva Nagaraju, V., Sai Greeshma, M., Revanth Reddy, B., Suresh Kumar, U., Surekha, C. (2019). A Simple and Enhanced Low-Light Image Enhancement Process Using Effective Illumination Mapping Approach. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_94
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DOI: https://doi.org/10.1007/978-3-030-00665-5_94
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