## 1 Introduction  
In the research of passive millimetre wave (PMMW) imaging, focal plane array (FPA) is widely applied for object detection, scene monitoring, and security checks, because of its fast and wide-range of imaging abilities. In the area of electromagnetic compatibility (EMC) detection, we adopt the FPA system to take an image of spatial electromagnetic leakage of electronic equipment (working in the microwave band), which can be called the passive focal-plane microwave (PFPM) imaging system, as shown in Figure 1. The reflector of the PFPM system is offset to offset center obscuring, and its aperture is much larger than that in PMMW to match the microwave wavelengths according to diffraction theory. Figure 1: Schematic diagram of an off-axis parabolic (OAP) imaging system. Compared with PMMW imaging, due to the much lower frequency of the source (3-6 GHz) and the limited aperture of the parabolic reflector, the PFPM imaging system is more diffraction-limited, so the image has larger blur degradation. Moreover, the aberration of the system caused by the off-axis parabolic (OAP) reflector is pretty serious, which increases the space-variant characteristic of the point spread function(PSF). The two reasons both make the blurred image restoration of the PFPM image much harder than PMMW. In the field of optical imaging, several restoration algorithms of the degraded image caused by space-variant blur have been proposed. There are three main kinds of methods: direct restoration, segmentation restoration, and space coordinate transformation restoration. The direct recovery method considers image degradation from a global perspective. It decomposes, collates, and compresses a large amount of PSF data into smaller storage quantities and performs recovery by an iterative or non-iterative method. Both [PERSON] and [PERSON] [1] proposed solving the SVPSF image restoration problem with the conjugate gradient method and experimenting with the 31 \(\times\) 31 image. In 1977, [PERSON] [2] proposed the Kalman filtering method, and it was subsequently revised and improved [3]. [PERSON] [4] used the projection onto convex set (POCS) method for space-variant blurred image restoration. [PERSON] [5] improved the singular value decomposition method and sped up the calculation. [PERSON] [6] used the interpolation method to process the PSF when performing image restoration on the Hubble telescope image. After analyzing the SVPSF with the diffraction limit and aberrations of the optical system, [PERSON] et al. [7] completed the image restoration using the Gauss-Seidel iterative algorithm. The segmentation algorithm divides the image into several sub-blocks whose PSF are considered space-invariant. Then, the space-invariant restoration method is used to complete the image restoration in each image sub-block, and finally, the sub-blocks are spliced to obtain the final image. [PERSON] and [PERSON] proposed an image block-based restoration algorithm in 1978 [8; 9]. [PERSON] [10] discussed, in detail, the effects of different segmentation methods on the image restoration quality, and the results were affected by the computational complexity and the edge ringing effect. [PERSON] et al. [11] used the image maximization algorithm to estimate the PSF after image segmentation, and then, used the spatial adaptive least squares method to complete image restoration. [PERSON] [12] proposed a new segmentation method based on the mean-shift algorithm to split the signal region out in an electromagnetic image. The coordinate transformation restoration (CTR) method is mainly used for the degradation of linear motion blur, radial symmetric blur, and so on. By transforming the image into a certain coordinate space where the point spread function is spatially invariant, restoration of degraded image can be simplified and achieved effectively and accurately. In reference [13], the author used polar-coordinate transformation to realize space-invariant transformation in the azimuth direction in optical imaging.