Stripe noise reduction in MODIS data: a variational approach

Ning Ma; Ze-ming Zhou; Li-min Luo; Min Wang

doi:10.1117/12.900759

8 September 2011 Stripe noise reduction in MODIS data: a variational approach

Ning Ma, Ze-ming Zhou, Li-min Luo, Min Wang

Proceedings Volume 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications; 81933C (2011) https://doi.org/10.1117/12.900759
Event: International Symposium on Photoelectronic Detection and Imaging 2011, 2011, Beijing, China

Abstract

According to the characteristics of MODIS data stripe noises, we propose a novel variational method for stripe noise reduction. First we find the detectors contaminated by stripe noises by separating MODIS data into several subimages due to the numbers of scan detectors. Then for subimages with stripe noises, we build an energy minimization problem by combining two energy terms to find the solution as the destriped result. The first energy term uses variational histogram matching method to remove detector-to-detector stripes and mirror side stripes while the second energy term uses non-linear anisotropic diffusion method to remove the random noise of noisy stripes. The gradient descent flow is applied to minimize the total energy functional and the numerical scheme is presented. Experimental results show that the method can reduce stripes noises effectively.

Citation Download Citation

Ning Ma, Ze-ming Zhou, Li-min Luo, and Min Wang "Stripe noise reduction in MODIS data: a variational approach", Proc. SPIE 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, 81933C (8 September 2011); https://doi.org/10.1117/12.900759

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