Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but the spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention Network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://github.com/LiPang/HSIGene.

翻译：高光谱图像（HSI）在农业和环境监测等多个领域发挥着至关重要的作用。然而，由于采集成本高昂，高光谱图像的数量有限，导致下游任务的性能下降。尽管最近的一些研究尝试利用扩散模型合成HSI，但它们仍然受限于HSI的稀缺性，影响了生成图像的可靠性和多样性。一些研究提出结合多模态数据以增强空间多样性，但无法保证光谱保真度。此外，现有的HSI合成模型通常不可控或仅支持单条件控制，限制了其生成准确可靠HSI的能力。为缓解这些问题，我们提出了HSIGene，一种基于潜在扩散并支持多条件控制的新型HSI生成基础模型，能够实现更精确可靠的HSI生成。为了在保持光谱保真度的同时增强训练数据的空间多样性，我们提出了一种基于空间超分辨率的新数据增强方法，即首先对HSI进行上采样，从而通过裁剪高分辨率HSI获得丰富的训练图像块。此外，为提高增强数据的感知质量，我们引入了一种新颖的两阶段HSI超分辨率框架，该框架首先应用RGB波段超分辨率，然后利用我们提出的矩形引导注意力网络（RGAN）进行引导式HSI超分辨率。实验表明，所提模型能够为去噪和超分辨率等下游任务生成大量逼真的HSI。代码和模型可在 https://github.com/LiPang/HSIGene 获取。