Volume 9 Issue 3
May  2016
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XU Ting-fa, SU Chang, LUO Xuan, BIAN Zi-yang. Underwater range-gated image denoising based on gradient and wavelet transform[J]. Chinese Optics, 2016, 9(3): 301-311. doi: 10.3788/CO.20160903.0301
Citation: XU Ting-fa, SU Chang, LUO Xuan, BIAN Zi-yang. Underwater range-gated image denoising based on gradient and wavelet transform[J]. Chinese Optics, 2016, 9(3): 301-311. doi: 10.3788/CO.20160903.0301

Underwater range-gated image denoising based on gradient and wavelet transform

doi: 10.3788/CO.20160903.0301
Funds:

Supported by International S&T Cooperation Program of China No.2014DFR1096

  • Received Date: 22 Jan 2016
  • Accepted Date: 28 Feb 2016
  • Publish Date: 25 Jan 2016
  • For the scattering effect of water, the laser spot, and other non-ideal imaging device, the image appears a large number of irregular granular noise. All of them increase the background noise of underwater range-gated images, blurring the target profile, obscuring details of the target, and reducing SNR. A denoising method based on gradient and wavelet transform is proposed. Firstly, the cosine wavelet transform is used to decompose the noisy image into many different frequency space image sets. For low frequency space image, a new image gradient enhancement method is used to improve the whole image's texture information. The LH or HL space images which have the information of non-uniform strips use the surface fitting method to eliminate the whole image's non-uniform strips. In the HH space denoising process, for the lower level space images, the non-local means method is used to preserve the whole image's similarity information, and for the upper space images, the fractional integral method is used to preserve the whole image's more details. Finally, the inverse wavelet transform is used to obtain the final image. Some contrast experiment are taken using underwater images from the long sink. The results show that the denoise method proposed in this paper can smooth the noise and preserve more texture of the image at the same time that comparing with other contrast methods. The objective evaluating index is improved by 6%.

     

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