用扩散模型修复超薄透镜的彩色成像畸变
Diffusion Algorithm for Metalens Optical Aberration Correction
- 双分支扩散模型融合结构图与色彩线索
- 重建图像保留高频细节并准确还原颜色
- 适合光学成像与超薄镜头系统研究者
金属透镜可实现超薄光学系统，但存在严重的空间变化光学畸变，尤其是色差，导致图像重建困难。本文提出一种新算法，利用金属透镜系统获取的两个输入——清晰的窄带灰度‘结构图像’和严重失真的‘色彩线索’图像——来重建锐利的全彩图像。方法基于预训练的 Stable Diffusion XL 框架，构建双分支扩散模型，融合两路信息。通过定量与定性对比验证，该方法显著优于现有去模糊与多光谱融合技术，有效恢复高频细节并准确还原图像色彩。
原文摘要 · Abstract (English)
Metalenses offer a path toward creating ultra-thin optical systems, but they inherently suffer from severe, spatially varying optical aberrations, especially chromatic aberration, which makes image reconstruction a significant challenge. This paper presents a novel algorithmic solution to this problem, designed to reconstruct a sharp, full-color image from two inputs: a sharp, bandpass-filtered grayscale ``structure image'' and a heavily distorted ``color cue'' image, both captured by the metalens system. Our method utilizes a dual-branch diffusion model, built upon a pre-trained Stable Diffusion XL framework, to fuse information from the two inputs. We demonstrate through quantitative and qualitative comparisons that our approach significantly outperforms existing deblurring and pansharpening methods, effectively restoring high-frequency details while accurately colorizing the image.
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