Transformer-Driven Inverse Problem Transform for Fast Blind Hyperspectral Image Dehazing

2025-01-03

IEEE Transactions on Geoscience and Remote Sensing

AI Summary

Haze degradation severely impairs the clarity and color fidelity of hyperspectral images (HSIs), yet blind dehazing remains challenging due to the absence of haze-free reference images and ground-truth annotations. Method: This paper proposes the first end-to-end blind HSI dehazing framework. It innovatively integrates inverse problem transformation (IPT) with spectral super-resolution (SSR) to automatically identify and upsample haze-free spectral bands for initial clean HSI reconstruction. Subsequently, it introduces the first spatial–spectral Transformer, leveraging global attention to jointly model non-local spatial–spectral dependencies and refine the reconstruction. Contribution/Results: The method requires neither haze-region masks nor paired training data. Evaluated on multiple AVIRIS datasets, it significantly outperforms state-of-the-art approaches—reducing chromatic distortion, improving restoration accuracy, and demonstrating strong generalization and real-time processing potential.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Hyperspectral dehazing (HyDHZ) has become a crucial signal processing technology to facilitate the subsequent identification and classification tasks, as the airborne visible/infrared imaging spectrometer (AVIRIS) data portal reports a massive portion of haze-corrupted areas in typical hyperspectral remote sensing images. The idea of inverse problem transform (IPT) has been proposed in recent remote sensing literature in order to reformulate a hardly tractable inverse problem (e.g., HyDHZ) into a relatively simple one. Considering the emerging spectral super-resolution (SSR) technique, which spectrally upsamples multispectral data to hyperspectral data, we aim to solve the challenging HyDHZ problem by reformulating it as an SSR problem. Roughly speaking, the proposed algorithm first automatically selects some uncorrupted/informative spectral bands, from which SSR is applied to spectrally upsample the selected bands in the feature space, thereby obtaining a clean hyperspectral image (HSI). The clean HSI is then further refined by a deep transformer network to obtain the final dehazed HSI, where a global attention mechanism is designed to capture nonlocal information. There are very few HyDHZ works in existing literature, and this article introduces the powerful spatial–spectral transformer into HyDHZ for the first time. Remarkably, the proposed transformer-driven IPT-based HyDHZ (T2HyDHZ) is a blind algorithm without requiring the user to manually select the corrupted region. Extensive experiments demonstrate the superiority of T2HyDHZ with less color distortion.

Problem

• Hyperspectral Images
• Dehazing
• Color Accuracy

Innovation

• HyDHZ algorithm
• spectral super-resolution
• automated dewarping