Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function
Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods.
 Singh, P.K., Panda, R.K., Sangwan, O.P.: A Critical Analysis on Software Fault Prediction Techniques, published in World Applied Sciences Journal, Vol. 33, No. 3, pp. 371–379, 2015.
 Singh, P. K., Agarwal, D., Gupta, A.: A Systematic Review on Software Defect Prediction, published in Computing for Sustainable Global Development (INDIACom), IEEE, pp. 1793– 97, 2015.
 Negi, S.S., Bhandari, Y.S.: A hybrid approach to Image Enhancement using Contrast Stretching on Image Sharpening and the analysis of various cases arising using histogram, published in Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–6, 2014.
 Wang, L.J., Huang, Y.C.: Non-linear image enhancement using opportunity costs, published in Second International Conference on Computational Intelligence Communication Systems and Networks (CICSyN), IEEE, pp. 256–261, 2010.
 Arunachalam, S., Khairnar, S.M., Desale, B.S.: Implementation of fast fourier transform and vedic algorithm for image enhancement. Appl. Math. Sci. 9(45), 2221–2234 (2015).
 Ramiz, M.A., Quazi, R.: Design of an efficient image enhancement algorithms using hybrid technique. Int. J. Recent Innov. Trends Comput. Commun. 5(6), 710–713 (2017)
 Pawar, M.M., Kulkarni, N.P.: Image resolution enhancement using multi-wavelet transforms with interpolation technique. IOSR J. Electr. Electron. Eng. 9(3), 9–13 (2014).
 Sumathi, M., Murthi, V.K.: Image enhancement based on discrete wavelet transform. IIOABJ 7(10), 12–15 (2016).
 Arya, A.R., Sreeletha, S.H.: Resolution enhancement of images using multi-wavelet and interpolation techniques. Int. J. Adv. Res. Comput. Commun. Eng. 5(7), 228–231 (2016).
 Badgujar, P.N., Singh, J.K.: Underwater image enhancement using generalized histogram equalization, discrete wavelet transform and KL-transform. Int. J. Innov. Res. Sci. Eng. Technol. 6(6), 11834–11840 (2017).
 Kaur, G., Vashist, S.: A robust approach for medical image enhancement using DTCWT. Int. J. Comput. Appl. 167(6), 26–29 (2017).
 HemaLatha, M., Vardarajan, S.: Resolution enhancement of low resolution satellite images using dual-tree complex wavelet transform. Int. J. Sci. Eng. Res. 8(5), 1361–1364 (2017).
 Kumar, B.P.S.: Image enhancement using discrete curvelet transform. Int. Res. J. Eng. Technol. 2(8), 1252–1259 (2015).
 Farzam, S., Rastgarpour, M.: An image enhancement method based on curvelet transform for CBCT-images. Int. J. Math. Comput. Phys. Electr. Comput. Eng. 11(6), 200–206 (2017).
 Fan, Z., Bi, D., Gao, S., He, L., Ding, W.: Adaptive enhancement for infrared image using shearlet frame. J. Opt. 18, 1–11 (2016).
 Tong, Y., Chen, J.: Non-linear adaptive image enhancement in wireless sensor networks based on nonsubsampled shearlet transform. EURASIP J. Wirel. Commun. Netw. 46 (2017).
 Favorskayaa, M.N., Savchinaa, E.I.: Content preserving watermarking for medical images using shearlet transform and SVD. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W4, pp. 101–108 (2017).
 Wu, C., Liu, Z., Jiang, H.: Catenary image enhancement using wavelet-based contourlet transform with cycle translation, published in Optik-International Journal for Light and Electron Optics, Vol. 125, No. 15, pp. 3922–3925, 2014.
 Premkumar, S., Parthasarathi, K.A.: An efficient approach for colour image enhancement using Discrete Shearlet Transform, published in 2nd International Conference on Current Trends in Engineering and Technology (ICCTET), IEEE, pp. 363–366, 2014.
 Bhattacharya, S., Gupta, S., Subramanian, V.K.: Localized image enhancement, published in Twentieth National Conference on Communications (NCC), IEEE, pp. 1–6, 2014.
 Dong-liang, P., An-Ke, X.: Degraded image enhancement with applications in robot vision, published in IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, pp. 1837–1842, IEEE, 2005.
 Xianghong, W., Shi, Y., Xinsheng, X.: An effective method to colour medical image enhancement, published in IEEE/ICME International Conference on Complex Medical Engineering, pp. 874–877, IEEE, 2007.
 Verma, A., Goel, S., Kumar, N.: Gray level enhancement to emphasize less dynamic region within image using genetic algorithm, published in 3rd International conference on Advance Computing Conference (IACC), pp. 1171–1176. IEEE, 2013.
 Khan, T.M., Khan, M.A., Kong, Y.: Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters, published in Optik-International Journal for Light and Electron Optics Vol. 125, No. 16, pp. 4206–4214, 2014.
 Shanmugavadivu, P., Balasubramanian, K.: Particle swarm optimized multi-objective histogram equalization for image enhancement, published in Optics Laser Technology, Vol. 57, pp. 243–251, 2014.
 Gorai, A.,Ghosh, A.: Hue-Preserving Color Image Enhancement Using Particle Swarm Optimization, published in IEEE, pp. 563–568, 2011.
 Benala, T.R., Jampala, S.D., Villa, S.H., Konathala, B.: A novel approach to image edge enhancement using artificial bee colony optimization algorithm for hybridized smoothening filters, published in IEEE, pp. 1071–1076, 2009.
 Hanumantharaju, M.C., Aradhya, V.N.M., Ravishankar, M., Mamatha, A.: A Particle Swarm Optimization Method for Tuning the Parameters of Multiscale Retinex Based Color Image Enhancement, published in ICACCI’12, Chennai, T Nadu, India, ACM, pp. 721–727, August 3–5, 2012.
 Zhou, X., Sun, G., Zhao, D., Wang, Z., Gao, L., Wang, X., Jin, Y.: A Fuzzy Enhancement Method for Transmission Line Image Based on Genetic Algorithm, published in Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 223–226, 2013.
 Singh, P.K., Sangwan, O.P., Sharma, A.: A Systematic Review on Fault Based Mutation Testing Techniques and Tools for Aspect-J Programs, published in 3rd IEEE International Advance Computing Conference, IACC-2013 at AKGEC Ghaziabad, IEEE Xplore, pp. 1455–1461, 2013.
 S. Park, S. Yu, M. Kim, K. Park and J. Paik, "Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement," in IEEE Access, vol. 6, pp. 22084-22093, 2018.
 http://blog.sina.com.cn/s/blog a0a06f190101cvon.html
 http://mcl.korea.ac.kr/projects/LDR/LDR TEST IMAGES DICM.zip