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Recently, magnetocardiography (MCG) has attracted increasing attention as a non-invasive and non-contact technique for detecting electrocardioelectric functions. However, the severe background noise makes it difficult to extract information. Variational Mode Decomposition (VMD), which is an entirely non-recursive model, is used to decompose the non-stationary signal into the intrinsic mode functions (IMFs). Traditional VMD algorithms cannot control the bandwidth of each IMF, whose quadratic penalty lacks adaptivity. As a result, baseline drift noise is still present or medical information is lost. In this paper, to overcome the unadaptable quadratic penalty problem, an improved VMD model via correlation coefficient and new update formulas are proposed to decompose MCG signals. To improve the denoising precision, this algorithm is combined with the interval threshold algorithm. First, the correlation coefficient is calculated, to determine quadratic penalty, in order to extract the first IMF made up of baseline drift. Then, the new update formulas derived from the variance that describes the noise level are used, to perform decomposition on the rest signal. Finally, the Interval thresholding algorithm is performed on each IMF. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

Advanced Geostationary Radiation Imager (AGRI) is one of the main payloads of the second-generation geostationary orbit meteorological satellite, FengYun-4A. Typically, the existence of variable stripe noise in the water vapor band remote sensing images of the AGRI greatly affects many applications, such as cloud detection, especially as one full disk image is separated into ten sub-images for transforming as soon as possible, so the denoising algorithm, which can reduce variable stripe noise and is adaptive to process using sub-images, must be built. In this paper, we propose an adaptive wavelet filter for image denoising. This approach introduces a new parameter termed weight sum variance of digital number probability (WSVODP), which is used to indicate the appropriate wavelet filter coefficients. WSVODP is only sensitive to the difference of observation targets of different sensors. Thus, our approach can learn appropriate wavelet filter coefficients fast and exactly. We built a real-world remote sensing image dataset from AGRI on FengYun-4A, and the experimental results on this dataset show that the proposed approach could effectively reduce the variable stripe noise from different observation targets. At the same time, an edge compensation method, which is fitted to the scanning model of the AGRI, is suggested to avoid ringing artifacts. Many applications, such as cloud detection with denoised images, show very good results. The proposed approach reduces the stripe noise adaptation, so the result is very steady even if the stripe noise varies with different targets, and edge compensation ensures that there are no obvious ringing artifacts in the full disk image joined by the ten sub-images.

I would like to dedicate this thesis to my beautiful and wonderful wife Yaqin Liu who always help me to become better and stronger. Without her unmitigated support in every possible way I would not have been able to accomplish this work. I would also like to thank my friends and family for their loving guidance and support during my study and research life. iii