基于统计比值差值排序滤波器的Sea WiFS图像椒盐噪声检测与消除

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基于统计比值差值排序滤波器的Sea WiFS图像椒盐噪声检测与消除

2024-07-16 15:55| 来源: 网络整理| 查看: 265

Due to some uncertain reasons

many seaWiFS satellite images are corrupted by impulse noise. In this paper

we firstly analyzed the characteristics of impulse noise and proposed a new rank ordered filter based on the difference of sequence of mean and standard deviation ratio

which is named as Statistical Ratio Rank Ordered Differences Filter (SRROD filter). Second

We described the impulse noise detection and removal algorithm in detail. Similar to traditional median filter

the processing of SRROD filter is implemented by a moving window concerning to different size of neighborhood. Compared with median filter and other existing filters

our filter could effectively remove impulse noises while preserving other valid pixels without or only with little modification

with the cost of about 10 times extra computing time than median filter. To better assess the noise removal quality

we have derived a more reasonable variable to estimate the image quality. That was the Effective Peak Signal to Noise Ratio( EPSNR )

instead of the traditional Peak Signal to Noise Ratio (PSNR) . The estimation of EPSNR also showed that much better improvement has been achieved with our algorithm than median filter. In our algorithm

through controlling the value of lower and upper threshold

different filter effect could be achieved. One of the key to successfully remove impulse noise is the way to choose an optimal threshold pair. Thus we also made fully discussion of finding an optimal threshold pair. Based on the estimation and assessment for the distribution map of the EPSNR according to different lower and upper threshold pairs

a nearly optimal threshold could be found. The Laplacian transformation was found very useful in finding this optimal threshold pair. The estimated optimal threshold pair was applyied to a full scene SeaWiFS image(Channel 2)and obtained a fairly good result

in which the result was also shown in our paper. Finally

some concluding remarks and limitations of our algorithm as well as the suggestions are given. The further work to be conducted also presented in the conclusion section.



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