Physics-prior and deep learning fusion for single-plane diffractive imaging in frequency-spatial domains

Traditional optical systems often require multiple lenses and substantial physical space to achieve high imaging quality. In contrast, single-plane diffractive optical elements (SPDOEs) enable compact, single-element imaging due to their unique imaging characteristics. This paper proposes a prior-guided computational imaging method tailored for SPDOEs. Specifically, a prior-guided attention-enhanced multiscale deblurring network (PAMDN) was developed to achieve high-quality image reconstruction. An SPDOE with an easily manufacturable f-number of 5 and a focal length of 50 mm was designed and fabricated. A full field-of-view (FOV) average wavefront aberration model was established, and diffraction efficiency under fabrication errors was analyzed. Based on these, RGB-channel prior PSF features and loss-function weights were constructed. Frequency-domain features were extracted via inverse filtering of the degraded image and prior PSF. A multiscale attention mechanism then fuses both spatial- and frequency-domain features for final image reconstruction. A small-sample training dataset was established using real-world images captured by the fabricated SPDOE and a high-quality industrial lens. Experimental results show that PAMDN improves the average peak signal-to-noise ratio (PSNR) by 30% over conventional PSF-based deconvolution methods, with gains exceeding 13 dB across various FOV regions. This paper provides a theoretical foundation for high-quality imaging with SPDOEs and offers a new perspective for the miniaturization of future optical systems.