Remote sensing image denoising technology based on FFDNet model

Long Gao; Shigang Hu; Zhijun Tang; Chaoyang Chen

doi:10.1117/12.2626858

14 February 2022 Remote sensing image denoising technology based on FFDNet model

Long Gao, Shigang Hu, Zhijun Tang, Chaoyang Chen

Proceedings Volume 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021); 121610Q (2022) https://doi.org/10.1117/12.2626858
Event: 4th International Conference on Informatics Engineering and Information Science, 2021, Tianjin, China

Abstract

Convolutional neural network is widely used in the field of image denoising, and FFDNet model has excellent performance in the field of image denoising. The denoising of remote sensing image is also one of the most basic preprocessing methods of remote sensing image. In this paper, FFDNet model is applied to remote sensing image denoising. Select a remote sensing image data set (UC combined land use data set), replace the natural noise with additive Gaussian white noise (AWGN), process it with different methods, compare it with DnCNN and VDNet, and analyze the comparison results. FFDNet has better performance.

Citation Download Citation

Long Gao, Shigang Hu, Zhijun Tang, and Chaoyang Chen "Remote sensing image denoising technology based on FFDNet model", Proc. SPIE 12161, 4th International Conference on Informatics Engineering & Information Science (ICIEIS2021), 121610Q (14 February 2022); https://doi.org/10.1117/12.2626858

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KEYWORDS

Image processing

Image denoising

Remote sensing

Signal to noise ratio

Convolutional neural networks

Interference (communication)

Convolution

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