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基于梯度和小波变换的水下距离选通图像去噪

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

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摘要

水体的散射效应、激光光斑、成像器件的非理想化等因素使得图像出现大量无规律粒状噪声, 它们增加了水下距离选通图像的背景噪声, 模糊了目标轮廓, 掩盖了目标细节, 降低了图像的信噪比。针对上述问题本文提出了一种基于梯度和小波变换的去噪方法。首先对图像进行余弦小波变换, 得到不同频率空间的图像集。低频空间引入新的图像梯度强化方法以提高图像的纹理信息量; 对应非均匀性条带的LH或HL空间做曲面拟合处理以消除非均匀性条带的影响; 在HH空间去噪过程中, 低层空间做非局部均值处理以保留图像相似信息, 高层空间做分数阶积分处理以保留图像细节信息。最后小波逆变换得到结果图像。从实验水槽中采集水下图像进行算法验证, 将改进方法与已有算法比对分析。实验表明, 本文所研究的水下去噪算法, 能够平滑噪声且更大限度地保留图像细节纹理, 在客观评价指标上提升了6%。

Abstract

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%.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP751.1

DOI:10.3788/co.20160903.0301

所属栏目:信息光学

基金项目:国家国际科技合作专项资助项目(No.2014DFR1096)

收稿日期:2016-01-22

修改稿日期:2016-02-28

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许廷发:北京理工大学 光电学院 光电成像技术与系统教育部重点实验室, 北京 100081
苏畅:北京理工大学 光电学院 光电成像技术与系统教育部重点实验室, 北京 100081
罗璇:北京理工大学 光电学院 光电成像技术与系统教育部重点实验室, 北京 100081
卞紫阳:北京理工大学 光电学院 光电成像技术与系统教育部重点实验室, 北京 100081

联系人作者:许廷发(xutingfa@163.com)

备注:许廷发(1968-), 男, 黑龙江肇东人, 博士, 教授, 博士生导师, 1992年、2000年于东北师范大学分别获得学士、硕士学位, 2004年于中国科学院长春光学精密机械与研究所获得博士学位, 2006年于华南理工大学电子与信息学院完成博士后工作, 主要从事光电成像探测与识别等方面的研究。

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