Opt Express. 2023 May 8;31(10):15889-15903. doi: 10.1364/OE.489157.
Fresnel zone aperture (FZA) lensless imaging encodes the incident light into a hologram-like pattern, so that the scene image can be numerically focused at a long imaging range by the back propagation method. However, the target distance is uncertain. The inaccurate distance causes blurs and artifacts in the reconstructed images. This brings difficulties for the target recognition applications, such as quick response code scanning. We propose an autofocusing method for FZA lensless imaging. By incorporating the image sharpness metrics into the back propagation reconstruction process, the method can acquire the desired focusing distance and reconstruct noise-free high-contrast images. By combining the Tamura of the gradient metrics and nuclear norm of gradient, the relative error of estimated object distance is only 0.95% in the experiment. The proposed reconstruction method significantly improves the mean recognition rate of QR code from 4.06% to 90.00%. It paves the way for designing intelligent integrated sensors.
菲涅尔区孔径（FZA）无透镜成像将入射光编码成类似全息图的图案，因此可以通过反向传播方法在长成像距离处对场景图像进行数值聚焦。然而，目标距离是不确定的。不准确的距离会导致重建图像出现模糊和伪影。这给目标识别应用带来了困难，例如快速响应码扫描。我们提出了一种用于 FZA 无透镜成像的自动对焦方法。通过将图像锐度度量值纳入反向传播重建过程中，该方法可以获取所需的聚焦距离并重建无噪高对比度图像。通过结合梯度的 Tamura 度量和梯度的核范数，实验中估计目标距离的相对误差仅为 0.95%。所提出的重建方法显著提高了 QR 码的平均识别率，从 4.06%提高到 90.00%。为设计智能集成传感器铺平了道路。
