DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models

TL;DR

DiffFuSR introduces a modular diffusion-based super-resolution pipeline for Sentinel-2 multispectral imagery, achieving high fidelity and spectral consistency across all bands, outperforming existing methods.

Contribution

The paper presents a novel diffusion model-based super-resolution framework for all Sentinel-2 bands, including a fusion network for accurate multispectral enhancement.

Findings

Outperforms state-of-the-art in reflectance fidelity and spectral consistency

Effective blind super-resolution with a robust degradation model

Significantly improves the resolution of 20 m and 60 m bands

Abstract

This paper presents DiffFuSR, a modular pipeline for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampling distance (GSD) of 2.5 meters. The pipeline comprises two stages: (i) a diffusion-based super-resolution (SR) model trained on high-resolution RGB imagery from the NAIP and WorldStrat datasets, harmonized to simulate Sentinel-2 characteristics; and (ii) a learned fusion network that upscales the remaining multispectral bands using the super-resolved RGB image as a spatial prior. We introduce a robust degradation model and contrastive degradation encoder to support blind SR. Extensive evaluations of the proposed SR pipeline on the OpenSR benchmark demonstrate that the proposed method outperforms current SOTA baselines in terms of reflectance fidelity, spectral consistency, spatial alignment, and hallucination suppression. Furthermore, the fusion network significantly outperforms classical and learned pansharpening approaches, enabling accurate enhancement of Sentinel-2's 20 m and 60 m bands. This work proposes a novel modular framework Sentinel-2 SR that utilizes harmonized learning with diffusion models and fusion strategies. Our code and models can be found at https://github.com/NorskRegnesentral/DiffFuSR.