Hybrid Refractive-Diffractive Lens with Reduced Chromatic and Geometric Aberrations and Learned Image Reconstruction

Abstract

In this paper, we present a hybrid refractive-diffractive lens that, when paired with a deep neural network-based image reconstruction, produces high-quality, real-world images with minimal artifacts, reaching a PSNR of 28 dB on the test set. Our diffractive element compensates for the off-axis aberrations of a single refractive element and has reduced chromatic aberrations across the visible light spectrum. We also describe our training set augmentation and novel quality criteria called “false edge level” (FEL), which validates that the neural network produces visually appealing images without artifacts under a wide range of ISO and exposure settings. Our quality criteria (FEL) enabled us to include real scene images without a corresponding ground truth in the training process.

Achromatic Imaging Systems with Flat Lenses Enabled by Deep Learning

“…Therefore, we have decided to adopt a more challenging evaluation criterion by comparing our results with those achieved by applying deblurring algorithms for chromatic aberrations caused by other lenses with significantly lower chromatic aberrations. In refs , , the U-net architecture was used on a small data set of images captured from a high-resolution monitor using a Hybrid refractive–diffractive lens system. Although the chromatic aberrations in a hybrid lens system are much smaller than in our case, the reported PSNR is 27.71, significantly lower than our result.…”

Chromatic Aberration Correction in Harmonic Diffractive Lenses Based on Compressed Sensing Encoding Imaging

“…Such collaborative efforts, as echoed in the work of [ 32 , 33 ], not only reinforce our findings but also pave the way for future innovations in optical imaging technologies. As underscored by [ 34 ], the continued exploration of diffractive optics and computational algorithms holds great promise for the field, heralding a new era of optical solutions that enrich both the scientific community and technological applications. Our future work will not be limited to imaging at three wavelengths but will expand to multi-wavelength, that is, multispectral imaging, to capture information across a broader range of frequencies.…”

Achromatic Single Metalens Imaging via Deep Neural Network

“…Due to significant material dispersion and dispersive responses of metasurfaces, different spectral components passing through metalenses will focus on disparate spatial planes, creating chromatic aberrations and negatively impacting image quality. − The resolution and transmission mismatch between color channels also leads to reduced color contrast and inaccurate color balancing, significantly degrading color-reproduction capabilities and the signal-to-noise ratio (SNR) of full-color imaging. Existing strategies to mitigate chromatic aberration include cascaded multilayer metalenses, − interleaving meta-atoms for different wavelengths, − metalens arrays, dispersion correction phase mask, ,− increased focusing depth, and computational optimization and correction of phase profiles. , But these approaches increase system complexity while sacrificing other performance metrics such as scalable high-yield fabrication, imaging quality at center wavelength, and freedom of material choices. Consequently, a single meta-lens solution capable of color-accurate aberration-free imaging under diverse operating conditions remains elusive.…”