Mixed Noise Suppression Algorithm Based on Developable Local Surface of Image

Manli WANG; Fengying MA; Changsen ZHANG

doi:10.11999/JEIT201096

Volume 43 Issue 11

Nov. 2021

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Manli WANG, Fengying MA, Changsen ZHANG. Mixed Noise Suppression Algorithm Based on Developable Local Surface of Image[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3291-3300. doi: 10.11999/JEIT201096

Citation:

Manli WANG, Fengying MA, Changsen ZHANG. Mixed Noise Suppression Algorithm Based on Developable Local Surface of Image[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3291-3300. doi: 10.11999/JEIT201096

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Mixed Noise Suppression Algorithm Based on Developable Local Surface of Image

doi: 10.11999/JEIT201096

School of Physics &Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China

Funds: The National Natural Science Foundation of China (52074305), The Science and Technology Research in Henan Province (212102210005), The Henan Polytechnic University Photoelectric Sensing and Intelligent Measurement and Control Provincial Program Laboratory Open Fund (HELPSIMC-2020-00X)

Received Date: 2020-12-30
Rev Recd Date: 2021-05-22

Available Online: 2021-06-07

Publish Date: 2021-11-23

Abstract

Abstract

In order to meet the requirement of low resource cost and mixed noise suppression for outdoor target detection based on rotor Unmanned Aerial Vehicle (UAV), a mixed noise suppression algorithm based on Developable Local Surface (DLS) is proposed. This algorithm realizes the complementary advantages of the developable local surface algorithm and the layered noise reduction algorithm, and achieves the noise reduction effect that the neither algorithm can reach. Firstly, the developable local surface of image is used to suppress salt & pepper noise and low-density Gaussian noise in the image to obtain a preliminary denoised image. Then, the layered noise reduction in the spatial domain and the Fourier domain is carried, removing Gaussian noise and maximize the preservation of image edges, textures and other details. Finally, iteratively developable local surface and layered noise reduction to remove further residual components of mixed noise to achieve the purpose of suppressing mixed noise in target detection images. The experimental results show that the proposed algorithm has certain advantages over the other seven algorithms in removing mixed noise, and its subjective visual index and objective data index statistics are superior to those of the other seven algorithms.
- Image denoising,
- Mixed noise,
- Developable local surface,
- Layered noise reduction,
- Iteratively denoising

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References(20)

References

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