# Diffusion Algorithm for Metalens Optical Aberration Correction

arXiv: https://arxiv.org/abs/2511.12689 · HTML: https://mlsanity.com/paper/2511.12689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 10 of 10)

- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting](https://mlsanity.com/paper/2307.12348/) (2023)
- [Hyperspectral pansharpening: a review](https://mlsanity.com/paper/1504.04531/) (2015)
- [DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring](https://mlsanity.com/paper/2103.09962/) (2021)
- [Thin On-Sensor Nanophotonic Array Cameras](https://mlsanity.com/paper/2308.02797/) (2023)
- [Kernel Diffusion: An Alternate Approach to Blind Deconvolution](https://mlsanity.com/paper/2312.02319/) (2023)
- [DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models](https://mlsanity.com/paper/2502.03810/) (2025)

## Abstract (arXiv)

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.