【摘要】 二次相位超透镜能够将入射光的旋转对称性转换为焦斑的平移对称性，在大视场成像方面具有重要潜力。然而，其固有的严重像差影响了实际成像质量。为此，本文构建了基于U-Net卷积神经网络的图像恢复模型，旨在通过结合后端图像处理的方法提升二次相位超透镜的成像性能。结果表明，在0°至85°半视场角范围内，恢复图像的成像质量获得显著提升——平均结构相似性(SSIM)从0.74提高至0.78以上，平均峰值信噪比(PSNR)从16.0 dB提升至21.5 dB以上。特别是在85°大角度入射下，恢复图像的SSIM仍然可达0.7455,PSNR超过20.0 dB，实现了170°视场的高对比度成像。该方法无需引入额外光学元件，显著提升了超透镜在增强现实/虚拟现实(AR/VR)及生物医学成像等紧凑型系统中的适用性。更多还原

【Abstract】 Quadratic phase metalens can transform the rotational symmetry of incident light into the translational symmetry of focal spots, showing significant potential for wide-field-of-view imaging. However, its inherent severe aberrations affect the actual image quality. To address this, this paper constructs an image restoration model based on a U-Net neural network, aiming to enhance the imaging performance of the quadratic phase metalens by incorporating post-processing methods. The results demonstrate that within the half-field-of-view range of 0° to 85°,the imaging quality of the restored images is significantly improved — the average structural similarity(SSIM)increases from 0.74 to above 0.78, and the average peak signal-to-noise ratio(PSNR) rises from 16.0 dB to above21.5 dB. Particularly at a large incident angle of 85°, the SSIM of the restored image can still reach 0.7455, with a PSNR exceeding 20.0 dB, achieving high-contrast imaging over a 170° field of view. This method does not require additional optical components, thus significantly enhancing the potential applicability of metalenses in compact systems such as augmented reality/virtual reality(AR/VR) and biomedical imaging.更多还原