Abstract
Image enhancement is a technique to give a better quality of an image in terms of its clarity, brightness, and to give the human eye comfortable to look at. There are different types of techniques to give good quality to an image. Global image contrast enhancement is one of the most commonly used techniques to enhance the quality of an image, but it has some disadvantages with the fact that it does not consider the local details of an image, that is the detailed texture of an image. In local contrast enhancement, it addresses the local details of an image and preserves the local details of the image. Local details of an image are very important while analyzing an image, which is that of the scientific study of an image like the image taken from planetary bodies, satellite image, and also in medical images. Local details of an image are very important for diagnosing a particular ailment. When we used either local contrast enhancement or global contrast enhancement alone, we faced the loss of brightness of the image. In order to address and reduce this discrepancy of individual enhancement methods, a new proposal that uses both these methods on the same image. First, the image is locally enhanced and the output is again processed by the global enhancement method, thereby giving a properly enhanced image without losing the brightness of the image. This enhancement method is simulated in MATLAB, and results are verified on the parameters of image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Celik, T., Tjahjadi, T.: Contextual and variational contrast enhancement. IEEE Trans. Image Process. 20(12), 3431–3441 (2011)
Chen, S., Ramli, A.: Preserving brightness in histogram equalization based contrast enhancement techniques. Digit. Signal Proc. 14(5), 413–428 (2004)
Premkumar, S., Parthasarathi, K.A.: An efficient approach for colour image enhancement using Discrete Shearlet Transform. In: IEEE International Conference on Current Trends in Engineering and Technology (ICCTET) (2014)
Lal, S., Chandra, M.: Efficient algorithm for contrast enhancement of natural images. Int. Arab J. Inf. Technol. 11(1), 95–102 (2014)
Chen, S., Ramli, A.: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans. Consum. Electron. 49(4), 1301–1309 (2003)
Chen, S., Suleiman, A.: Scalable global histogram equalization with selective enhancement for photo processing. In: Proceedings of the 4th International Conference on Information Technology and Multimedia, Malaysia, pp. 744–752 (2008)
Pathak, S.S., Dahiwale, P., Padole, G.: A combined effect of local and global method for contrast image enhancement. In: IEEE International Conference on Engineering and Technology (ICETECH) (2015)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Publication
Jung, C., Wang, X.: Detail-Preserving Tone Mapping for Low Dynamic Range Displays with Adaptive Gamma Correction, pp. 1–5 (2015)
Li, X., Yang, Z., Shang, M., Hao, J.: Underwater Image Enhancement via Dark Channel Prior and Luminance Adjustment, pp. 1–5 (2016)
Vani, V., Prashanth, K.V.M.: Color image enhancement techniques in Wireless Capsule Endoscopy. In: International Conference on Trends in Automation, Communications and Computing Technology (I-TACT-15), pp. 1–6 (2015)
Su, H., Jung, C.: Low light image enhancement based on two-step noise suppression. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1977–1981 (2017)
Chondro, P., Hu, H.-C., Hung, H.-Y., Chang, S.-Y., Li, L.P.-H.: An effective occipitomental view enhancement based on adaptive morphological texture analysis. IEEE J. Biomed. Health Inform. 21(4), 1105–1113 (2017)
Shi, W., Chen, C., Jiang, F., Zhao, D., Shen, W.: Group-based sparse representation for low lighting image enhancement. In: IEEE International Conference on Image Processing (ICIP), pp. 4082–4086 (2016)
Haicheng, W., Mingxia, X., Ling, Z., Miaojun, W., Xing, W., Xiuxia, Z., Bai, Z.: Study on monitoring technology of UAV aerial image enhancement for burning straw. In: Chinese Control and Decision Conference (CCDC), pp. 4321–4325 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Srinivas, P.V.V.S., Maguluri, L.P., Syamala, M. (2018). An Efficient Image Enhancement Method for Dark Images. In: Tiwari, B., Tiwari, V., Das, K., Mishra, D., Bansal, J. (eds) Proceedings of International Conference on Recent Advancement on Computer and Communication . Lecture Notes in Networks and Systems, vol 34. Springer, Singapore. https://doi.org/10.1007/978-981-10-8198-9_48
Download citation
DOI: https://doi.org/10.1007/978-981-10-8198-9_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8197-2
Online ISBN: 978-981-10-8198-9
eBook Packages: EngineeringEngineering (R0)