As going deep under the water nothing can be seen properly as well as it is difficult to identify any substance residing or present under water. This survey basically focuses on the detection of the underwater image which are taken through various self-ruling submerged vehicles and remotely controlled vehicles, in order to improve the quality of the pictures. The factors include the low contrast, blur, non-uniform lighting and faded colors. This paper analyzed an image enhancement technique along with the image restoration technique that will help to acquire images that are of better quality. The algorithms applied on the degraded images comprises of two domains- Spatial Domain Methods, Frequency Domain Methods. The literature reviews used in this paper explained that the preprocessing algorithms used by various authors uses a standard filter techniques contain different combinations. The survey includes analysis in terms of qualitative and quantitative factors on hundreds of underwater images. The images taken in offshore water characterized by a heavy concentration of colored dissolved organic matter and total suspended matter, thus various methods have been applied on the images in a proper way so as to obtain a fresh image.

Offshore, underwater image restoration, under water imaging, underwater optical model

[1] Yang, Miao, Arcot Sowmya, ZhiQiang Wei, and Bing Zheng. Toward the ocean Underwater Image Restoration Using Reflection-Decomposition-Based Transmission Map Estimation. IEEE Journal of Oceanic Engineering (2019).
[2] Galdran, Adrian, David Pardo, Artzai Picón, and Aitor Alvarez-Gila. Customized red-channel submerged picture revamping. Journal of Visual Communication and Image Representation26 (2015): 132-145.
[3] Yang, Miao, and Arcot Sowmya. A submerged shading picture quality evaluation metric. IEEE Transactions on Image Processing 24, no. 12 (2015): 6062-6071.
[4] Alan J. Searcher and Robbert van Vossen Sonar target update by shrinkage of questionable wavelet coefficients. The Journal of the Acoustical Society of America, 2014.
[5] Priyadharsini, R., T. Sree Sharmila, and V. Rajendran. Submerged acoustic picture redesign using wavelet and KL change. In 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 563-567. IEEE, 2015.
[6] Melkamu Hunegnaw Asmare, Vijanth S. Asirvadam, Ahmad Fadzil M. Hani Image redesign subject to contourlet change. Appropriated on the web: 20 March 2014 © Springer-Verlag London 2014.
[7] Achyuth Rao MV and Prasanta Kumar Ghosh , Senior Member, IEEE. Glottal Inverse Filtering Using Probabilistic Weighted Linear Prediction. IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 27, NO. 1, JANUARY 2019.
[8] Selvakumari, NA Sheela, and V. Radha. A HYBRID APPROACH FOR NOISE REDUCTION USING WIENER FILTER AND WAVELET TRANSFORM. Worldwide Journal of Pure and Applied Mathematics 119, no. 16 (2018): 731-743.
[9] Murthy, Chidananda, M. Z. Kurian, and H. S. Guruprasad. Execution appraisal of picture remaking methods for close examination with and without hullabaloo. In 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 282-287. IEEE, 2015.
[10] Hitam, M.S., Yussof, W.N.J.W. also, Awalludin, E.A., 2013. Mix separate limited versatile histogram evening out for submerged picture improvement. IEEE International Conference on Computer Applications Technology (ICCAT), Sousse, 20-22 January 2013, pp.1-5.
[11] Airaksinen, Manu, Tuomo Raitio, Brad Story, and Paavo Alku. Semi shut stage glottal inverse isolating examination with weighted straight figure. IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, no. 3 (2014): 596-607.
[12] Auvinen, Harri, Tuomo Raitio, Manu Airaksinen, Samuli Siltanen, Brad H. Story, and Paavo Alku. Modified glottal inverse isolating with the Markov chain Monte Carlo procedure. PC Speech and Language 28, no. 5 (2014): 1139-1155.
[13] Wu, X. furthermore, Li, H., 2013. A prompt and wide model for submerged picture evolving. Proceeding of the IEEE, International Conference on Information and Automation, Yinchuan, 26-28 August 2013, pp.699-704.
[14] Peng, Yan-Tsung, Xiangyun Zhao, and Pamela C. Cosman. Single submerged picture update using significance estimation subject to dimness. In 2015 IEEE International Conference on Image Processing (ICIP), pp. 4952-4956. IEEE, 2015.
[15] Yang, Miao, and Arcot Sowmya. A submerged shading picture quality evaluation metric. IEEE Transactions on Image Processing 24, no. 12 (2015): 6062-6071.
[16] Aouinti, Fouad, Mbarek Nasri, Mimoun Moussaoui, and Bouchta Bouali. An improved richardson-lucy figuring subject to inherited strategy for satellite picture modifying. Int. Center Easterner J. Inf. Technol. 15, no. 4 (2018): 715-720.