In this paper, the infrared image of power equipment contains noise, blur and so on, which makes it impossible to accurately
judge and locate the infrared fault. An infrared image enhancement algorithm based on grey wolf adaptive non local mean
denoising and adaptive fuzzy enhancement is proposed. Firstly, grey wolf adaptive non local mean denoising is used to
denoise the initial infrared image, and then NSCT transform is performed. After the transform, grey wolf algorithm is used
to optimize the fuzzy parameters of high-frequency components, and then enhancement is performed; the low frequency
component is linearly enhanced. Then the NSCT inverse transform is performed. After the algorithm verification, it is
shown that the algorithm is effective in infrared image denoising and enhancement, and the evaluation index also verifies
the effectiveness of the algorithm.
Freeform surfaces have advantages on balancing asymmetric aberration of the unobscured push-broom imager. However, since the conventional paraxial aberration theory is no longer appropriate for the freeform system design, designers are lack of insights on the imaging quality from the freeform aberration distribution. In order to design the freeform optical system efficiently, the contribution and nodal behavior of coma and astigmatism introduced by Standard Zernike polynomial surface are discussed in detail. An unobscured three-mirror optical system with 850 mm effective focal length, 20°× 2° field of view (FOV) is designed. The coma and astigmatism nodal positions are moved into the real-FOV by selecting and optimizing the Zernike terms pointedly, which has balanced the off-axis asymmetric aberration. The results show that the modulation transfer function (MTF) is close to diffraction limit, and the distortion throughout full-FOV is less than 0.25%. At last, a computer-generated hologram (CGH) for freeform surface testing is designed. The CGH design error RMS is lower than λ/1000 at 632.8 nm, which meets the requirements for measurement.
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