DifuzCam replacing camera lens with a mask and a diffusion model for generative AI based flat camera design.

ABSTRACT: Recent advances in lensless, flat camera designs hold the promise of significantly reducing size and weight by replacing bulky lenses with thin optical elements that modulate incoming light. However, recovering high-quality images from the raw sensor measurements of such systems remains challenging. We address this limitation by introducing a novel reconstruction framework that leverages a pre-trained diffusion model, guided by a control network and a learnable separable transformation. This approach delivers high-fidelity images, achieving state-of-the-art performance in both objective and perceptual metrics. Our method achieves 20.43 PSNR, 0.612 SSIM, and 0.237 LPIPS on the FlatNet dataset, representing improvements of 9.6%, 18.1%, and 26.4% respectively over the previous state-of-the-ar

SUBMITTER: Yosef E 

PROVIDER: S-EPMC12675501 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature