Zobeda Hatif Naji,
Department of Intelligent Systems, Moscow Institute of Physics and Technology, Moscow, Russia
Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia
SYNCHROINFO JOURNAL. Volume 8, Number 2 (2022). P. 19-23.
Image enhancement is a type of image processing that improves the image’s suitability for specific uses. Image enhancement’s primary goal is to improve an image’s visual look, or to provide a “higher transform features of an image”. The goal of the paper is to analyze and formulate several image enhancing strategies that can be used in a variety of medical applications. A survey of various picture enhancement techniques is presented in this work. More specifically, the suggested research would focus on improving medical photos captured in low-light conditions, foggy environments, and speckle noise, among other things. Developing algorithms to aid clinicians in diagnosing the disease at its earliest stages.
Keywords: ANN, FuzzayLogic, Image enhancement, Image noising.
 B. Jabber, J. Lingampalli, C. Z. Basha, and A. Krishna, “Detection of covid-19 patients using chest x-ray images with convolution neural network and mobile net,” Proc. 3rd Int. Conf. Intell. Sustain. Syst. ICISS 2020, pp. 1032-1035, 2020.
 D. Sharifrazi et al., “Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images,” Biomed. Signal Process. Control, vol. 68, 2021.
 B. K. Umri, M. Wafa Akhyari, and K. Kusrini, “Detection of COVID-19 in Chest X-ray Image using CLAHE and Convolutional Neural Network,” 2020 2nd Int. Conf. Cybern. Intell. Syst. ICORIS 2020, 2020.
 H. M. El-Bakry, S. Abdelghany, A. A. Albahbah, and S. Abd-Elgahany, “Detection of caries in panoramic dental X-ray images using back-propagation neural network,” Researchgate.Net, vol. 7, no. 5, pp. 2249-071, 2016.
 V. Kumar and A. Saini, “Detection system for lung cancer based on neural network : X-Ray validation performance,” Int. J. Enhanc. Res. Manag. Comput. Appl., vol. 2, no. 9, pp. 40–47, 2013.
 V. Kumar and C. Science, “Neural Network Based Approach for Detection of Abnormal Regions of Lung Cancer in X-Ray Image,” vol. 1, no. 5, pp. 1-7, 2012.
 C. M. A. K. Zeelan Basha, T. Maruthi Padmaja, and G. N. Balaji, “Automatic X-ray image classification system,” Smart Innov. Syst. Technol., vol. 78, pp. 43-52, 2018.
 M. Daliri, H. Abrishami Moghaddam, S. Ghadimi, M. Momeni, F. Harirchi, and M. Giti, “Skull segmentation in 3D neonatal MRI using hybrid Hopfield Neural Network,” 2010 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC’10, no. January 2016, pp. 4060-4063, 2010.
 C.-Y. Chang, “Two-layer competitive based Hopfield neural network for medical image edge detection,” Opt. Eng., vol. 39, no. 3, p. 695, 2000.
 H. Haußecker and H. R. Tizhoosh, Fuzzy Image Processing, no. December. 2000.
 A. Kaur and G. Kaur, “A review on image enhancement with deep learning approach,” Accent. Trans. Image Process. Comput. Vis., vol. 4, no. 11, pp. 16-20, 2018.
 K. G. Lore, A. Akintayo, and S. Sarkar, “LLNet: A deep autoencoder approach to natural low-light image enhancement,” Pattern Recognit., vol. 61, pp. 650-662, 2017.
 A. H. Lone and A. N. Siddiqui, “Noise models in digital image processing,” Glob. Sci-Tech, vol. 10, no. 2, p. 63, 2018.
 https://www.google.com/url?sa=i&url=https%3A%2F%2 Fselectstar-ai.medium.com%2Fdifferent-types-of-neural-networks-cnn-rnn-a91b27babfa3&psig=AOvVaw2jw6QqCY7Uzyt9 FuenLfXj &ust=1649456979180000&srce=images&cd=vfe& ved=2ahUKEwjmtabgIP3AhXSwAIHHbq0DDUQr4kDegUIARCdAg.