Filtering images contaminated with pep and salt type noise with pulse-coupled neural networks

Zhang, Junying; Lu, Zhijun; Shi, Lin; Dong, Jiyang; Shi, Meihong

doi:10.1360/03ye0168

Cited by 12 publications

(14 citation statements)

References 18 publications

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“…Furthermore, compared with the conventional window-based filtering method, an adaptive filtering method was developed. The noisy pixels are first located and then filtered based on the output of the pulse coupled neural network [21] . For this study, depending on both visual inspection and accuracy assessment, we consider that the optimal window size is the peak point of overall accuracy.…”

Section: Discussionmentioning

confidence: 99%

Optimization of post-classification processing of high-resolution satellite image: A case study

Dong

et al. 2006

SCI CHINA SER E

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The application of remote sensing monitoring techniques plays a crucial role in evaluating and governing the vast amount of ecological construction projects in China. However, extracting information of ecological engineering target through high-resolution satellite image is arduous due to the unique topography and complicated spatial pattern on the Loess Plateau of China. As a result, enhancing classification accuracy is a huge challenge to high-resolution image processing techniques. Image processing techniques have a definitive effect on image properties and the selection of different parameters may change the final classification accuracy during post-classification processing. The common method of eliminating noise and smoothing image is majority filtering. However, the filter function may modify the original classified image and the final accuracy. The aim of this study is to develop an efficient and accurate post-processing technique for acquiring information of soil and water conservation engineering, on the Loess Plateau of China, using SPOT image with 2.5 m resolution. We argue that it is vital to optimize satellite image filtering parameters for special areas and purposes, which focus on monitoring ecological construction projects. We want to know how image filtering influences final classified results and which filtering kernel is optimum. The study design used a series of window sizes to filter the original classified image, and then assess the accuracy of each output map and image quality. We measured the relationship between filtering window size and classification accuracy, and optimized the post-processing techniques of SPOT5 satellite images. We conclude that (1) smoothing with the majority filter is sensitive to the information accuracy of soil and water conservation engineering, and (2) for SPOT5 2.5 m image, the 5×5 pixel majority filter is most suitable kernel for extracting information of ecological construction sites in the Loess Plateau of China.

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Section: Discussionmentioning

confidence: 99%

Optimization of post-classification processing of high-resolution satellite image: A case study

Dong

et al. 2006

SCI CHINA SER E

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“…Generally, Gaussian noise is very difficult to remove because of its random distributing property. However, the PCNN can deal with those noises via different strategies [41][42][43][44][45][46][47][48][49][50][51]. The Gaussian noised image is restored by the PCNN in virtue of median algorithm and step-bystep modifying algorithm [41].…”

Section: Image Denoisingmentioning

confidence: 99%

“…In addition, researchers attempt to combine the PCNN with other new techniques such as fuzzy, rough set theory and morphology to depress noises [47][48][49][50][51]. For instance, noises are reduced using the PCNN and fuzzy algorithm in [47].…”

Section: Image Denoisingmentioning

confidence: 99%

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Review of pulse-coupled neural networks

Wang

Cheng

et al. 2010

Image and Vision Computing

225

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“…Pulse-coupled neural network (pCNN) is widely used in image processing because of its biological optical features [4][5][6][7][8]. It has been frequently used in removing salt and pepper noise and shows good filtering perfonnance [7][8].…”

Section: Introductionmentioning

confidence: 99%

A new method of denoising mixed noise using limited grayscale pulsed couple neural network

Cheng

Zhang

et al. 2011

Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference

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A new method of removing mixed noise of Gaussian noise and salt and pepper noise was proposed based on extreme grayscale and pulse-coupled neural network (pCNN). First, salt and pepper noise was identified according to extreme grayscale value and then the noise was removed by mean filter while keeping the noise-free pixels unchanged so the proposed method is effective on preserving the image edges and details. Gaussian noise is identified by using the time matrix of PCNN. Finally Gaussian noise was filtered by adaptively selected filters based on variable step. Experimental results show that the proposed method has advantages in terms of filtering effects, adaptiveness and preserving image details compared to traditional methods.

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Filtering images contaminated with pep and salt type noise with pulse-coupled neural networks

Cited by 12 publications

References 18 publications

Optimization of post-classification processing of high-resolution satellite image: A case study

Optimization of post-classification processing of high-resolution satellite image: A case study

Review of pulse-coupled neural networks

A new method of denoising mixed noise using limited grayscale pulsed couple neural network

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