Compact Neural Pancake Camera for High-Perceptual-Quality Foveated Imaging

Pancake catadioptric optics utilize optical folding to effectively reduce the optical-path thickness in virtual-reality display systems. However, limited optical throughput and optical degradation make image reconstruction for pancake cameras severely ill-posed, hindering broader imaging applications. In this article, we propose a neural pancake camera with adaptive-prior deconvolution, achieving compact, high-perceptual-quality imaging. By introducing latent-space projection, adaptive prior deconvolution alleviates the trade-off between pixel fidelity and perceptual quality and addresses the excessive smoothing inherent in conventional pixelwise optimization. The proposed neural pancake camera reduces the ratio of axial length to physical aperture diameter by 3.2 times compared with other flat cameras with high imaging quality. Experiments and ablation studies substantiate that the proposed adaptive prior deconvolution improves perceptual quality by 70%, as measured by CLIP-IQA, while also outperforming the state-of-the-art deep learning models on pixel-level fidelity. As a representative application of the proposed neural Pancake camera, this work further showcases bioinspired foveated imaging, highlighting its potential for bandwidth-efficient imaging in next-generation edge and portable devices.