عنوان مقاله [English]
Absorption and scattering of light from water, and also in-water particles, are two main factors which impose the blurring and noise on captured images from underwater objects. In this manner, the working range of underwater optical imaging (UOI) systems is limited by these phenomena. Wavelet de-noised deconvolution and Fourier regularized deconvolution are two well-known software methods which have already been used for restoring the blurred-noisy underwater images. Nevertheless, the results of both of these software methods are very noise sensitive. Hybrid Fourier wavelet deconvolution is another image processing method which simultaneously benefits from the advantages of both wavelet de-noised deconvolution and Fourier regularized deconvolution techniques. In this paper, we propose to employ the hybrid Fourier wavelet method for underwater images restoration. Computer simulations show the superior performance of this technique to utilize in the UOI systems in comparison with the previously used wavelet de-noised deconvolution and Fourier regularized deconvolution methods.
 Amer KO, Elbouz M, Alfalou A, Brosseau C, Hajjami J. Underwater optical image processing: a comprehensive review. Mobile networks and applications. 2017;22(6):1204-1211.
 Prabhakar CJ, Kumar PU. An image based technique for enhancement of underwater images. International Journal of Machine Intelligence. 2011;3(4):217-224.
 Schettini R, Corchs S. Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP Journal on Advances in Signal Processing. 2010;1: 746052.
 Li Y, Zhang Y, Xu X, He L, Serikawa S, Kim H.Dust removal from high turbid underwater images using convolutional neural networks. Optics & Laser Technology.2019;110:2-6.
 WangM, ShudaoZh, WeiY.Blurred image restoration using knife-edge function and optimal window Wiener filtering. PloS one. 2018;13(1):e0191833.
 Fan F, Yang K, Xia M, Li W, Fu B, Zhang W. Underwater image restoration by means of blind deconvolution approach. Frontiers of Optoelectronics in China. 2010; 3(2):169-178.
 Fan F, Yang K, Xia M, Li W, Fu B, Zhang W. Comparative study on several blind deconvolution algorithms applied to underwater image restoration. Optical review. 2010;17(3):123-129.
 Zhishen L, Tianfu D, Gang W. ROV based underwater blurred image restoration. Journal of Ocean University of Qingdao. 2003;2(1):85-88.
 Jian S, Wang W. Study on underwater image denoising algorithm based on wavelet transform. Journal of physics. 2017;806(1):1-10.
 Mertens LE, Replogle FS. Use of point spread and beam spread functions for analysis of imaging systems in water. JOSA. 1977;67(8):1105-1117.
 Hou W, Gray DJ, Weidemann AD, Arnone RA. Comparison and validation of point spread models for imaging in natural waters. Optics Express. 2008;16(13):9958-9965.
 Chen Y, Xia M, Li W, Zhang X, Yang K. Comparison of point spread models for underwater image restoration. Optik. 2012;123(9):753-757.
 Gray DJ. Order-of-scattering point spread and modulation transfer functions for natural waters. Applied optics. 2012;51(28):6753-6764.
 Neelamani R, Choi H, Baraniuk R. ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems. IEEE Transactions on signal processing. 2004;52(2):418-433.
 Bahrampour AR, Askari AA. Fourier-wavelet regularized deconvolution (ForWaRD) for lidar systems based on TEA–CO2 laser. Optics communications. 2006; 257(1): 97-111.
 Bahrampour AR, Moosavi A, Bahrampour MJ, Safaei L. Spatial resolution enhancement in fiber Raman distributed temperature sensor by employing ForWaRD deconvolution algorithm.Optical Fiber Technology. 2011;17(2): 128-134.
 Langer M, Cloetens P, Peyrin F. Fourier-wavelet regularization of phase retrieval in x-ray in-line phase tomography. JOSA A. 2009; 26(8):1876-1881.
 Zhou Z, Gao F, Zhao H, Zhang L. Application of Fourier-wavelet regularized deconvolution for improving image quality of free space propagation x-ray phase contrast imaging. Physics in Medicine & Biology. 2012;57(22):7459.
 Wells WH. Theory of small angle scattering. Optics of the sea. IEEE. 1973;61.
 Mallat S. A wavelet tour of signal processing. 3thed, Academic press;2009.
 Bos AA, Malkasse JP, Kervern G. A preprocessing framework for automatic underwater images denoising. European Conference on Propagation and Systems. 2005 Mar; Brest, France.
 Liu Zh, Yu Y, Zhang K, Huang H. Underwater image transmission and blurred image restoration. Optical Engineering. 2001; 40(6):1125-1131.
 Donoho DL, Johnstone JM. Ideal spatial adaptation by wavelet shrinkage. Biometrika. 1994;81(3):425-455.
 Wang K, Hu Y, Chen J, Wu X, Zhao X, Li Y. Underwater image restoration based on a parallel convolutional neural network. Remote sensing. 2019 Jan;11(1591):1-21.
 Anwar S, Li C, Porikli F. Deep underwater image enhancement. arXiv preprint arXiv:1807.03528. 2018 Jul 10.
 Hou W, Gray DJ, Weidemann AD, Fournier GR, Forand JL. Automated underwater image restoration and retrieval of related optical properties. In2007 IEEE International Geoscience and Remote Sensing Symposium. 2007 Jul 23: 1889-1892.