Signal Processing and Image Restoration of Magnetic Resonance Microscope
Magnetic resonance force microscopy (MRFM) is a three-dimensional molecular imaging instrument with nanometer resolution. It combines magnetic resonance imaging technology with scanning probe microscopy. It has three-dimensional detection ability of magnetic resonance imaging and nano-resolution ability of scanning probe microscopy. It can detect single nucleus spin. However, there are some problems in the practical application of magnetic resonance force microscopy, such as slow scanning speed, susceptibility to noise and slow imaging speed. To solve these problems, it depends on the application of effective and exquisite signal processing methods and image processing methods. This paper studies some signal processing and image processing methods involved in magnetic resonance force microscopy, including four aspects: parameter estimation of noisy sinusoidal signal, application of ARMA model in system identification of micro-cantilever, restoration of spin density image of sample, design of spread spectrum and despreading scheme. In order to recover the spin density image from the degraded image, the imaging mechanism of magnetic resonance force microscopy (MRFM) and the method of restoring the spin density image are studied in this paper. According to the imaging mechanism of magnetic resonance force microscopy, the expression of point spread function is derived. When the point spread function is easy to obtain, considering the ill-conditioned nature of the image restoration problem, a non-blind image restoration algorithm based on sparsity constraints is used to restore the image; when the point spread function is not easy to obtain, the blind image restoration algorithm is used to restore the image, and the point spread function and the spin density image are restored at the same time. A scheme of spread spectrum and despreading is proposed, which can spread the input signal of magnetic resonance force microscopy and despread the output signal. In this paper, the theory of signal processing is used to study the anti-jamming performance of this scheme. Compared with the scheme without spreading spectrum, it is found that the proposed scheme has better anti-jamming performance under certain conditions. Finally, simulation experiments are used to prove the effectiveness of this scheme.