Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance

TL;DR

This paper presents SSC-HSR, a novel framework for hyperspectral image super-resolution guided by unaligned RGB references, which improves alignment accuracy and spectral-texture interaction for better super-resolution results.

Contribution

The paper introduces a two-stage image alignment with synthetic generation and a feature aggregation with spectral attention, enhancing unaligned RGB-guided hyperspectral super-resolution.

Findings

Outperforms state-of-the-art methods on multiple datasets.

Achieves more accurate spatial and spectral reconstruction.

Effectively handles unaligned reference images.

Abstract

Hyperspectral images super-resolution aims to improve the spatial resolution, yet its performance is often limited at high-resolution ratios. The recent adoption of high-resolution reference images for super-resolution is driven by the poor spatial detail found in low-resolution HSIs, presenting it as a favorable method. However, these approaches cannot effectively utilize information from the reference image, due to the inaccuracy of alignment and its inadequate interaction between alignment and fusion modules. In this paper, we introduce a Spatial-Spectral Concordance Hyperspectral Super-Resolution (SSC-HSR) framework for unaligned reference RGB guided HSI SR to address the issues of inaccurate alignment and poor interactivity of the previous approaches. Specifically, to ensure spatial concordance, i.e., align images more accurately across resolutions and refine textures, we construct a Two-Stage Image Alignment with a synthetic generation pipeline in the image alignment module, where the fine-tuned optical flow model can produce a more accurate optical flow in the first stage and warp model can refine damaged textures in the second stage. To enhance the interaction between alignment and fusion modules and ensure spectral concordance during reconstruction, we propose a Feature Aggregation module and an Attention Fusion module. In the feature aggregation module, we introduce an Iterative Deformable Feature Aggregation block to achieve significant feature matching and texture aggregation with the fusion multi-scale results guidance, iteratively generating learnable offset. Besides, we introduce two basic spectral-wise attention blocks in the attention fusion module to model the inter-spectra interactions. Extensive experiments on three natural or remote-sensing datasets show that our method outperforms state-of-the-art approaches on both quantitative and qualitative evaluations.