Hyperspectral Image Generation with Unmixing Guided Diffusion Model

• URL: http://arxiv.org/abs/2506.02601v1
• Date: Tue, 03 Jun 2025 08:27:10 GMT
• Title: Hyperspectral Image Generation with Unmixing Guided Diffusion Model
• Authors: Shiyu Shen, Bin Pan, Ziye Zhang, Zhenwei Shi,
• Abstract summary: Diffusion models are popular for their ability to generate high-quality samples.<n>We propose a novel diffusion model guided by hyperspectral unmixing.<n>Our model is capable of generating high-quality and diverse hyperspectral images.
• Score: 21.078386095749398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, hyperspectral image generation has received increasing attention, but existing generative models rely on conditional generation schemes, which limits the diversity of generated images. Diffusion models are popular for their ability to generate high-quality samples, but adapting these models from RGB to hyperspectral data presents the challenge of high dimensionality and physical constraints. To address these challenges, we propose a novel diffusion model guided by hyperspectral unmixing. Our model comprises two key modules: an unmixing autoencoder module and an abundance diffusion module. The unmixing autoencoder module leverages unmixing guidance to shift the generative task from the image space to the low-dimensional abundance space, significantly reducing computational complexity while preserving high fidelity. The abundance diffusion module generates samples that satisfy the constraints of non-negativity and unity, ensuring the physical consistency of the reconstructed HSIs. Additionally, we introduce two evaluation metrics tailored to hyperspectral data. Empirical results, evaluated using both traditional metrics and our proposed metrics, indicate that our model is capable of generating high-quality and diverse hyperspectral images, offering an advancement in hyperspectral data generation.

Related papers

• Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements [12.90606332269204]
  We formulate hyperspectral image (HSI) reconstruction as a Bayesian inference problem.<n>We propose a framework, HSDiff, that utilizes an unconditionally trained, pixel-level diffusion prior and posterior diffusion sampling.<n>We demonstrate that HSDiff offers a complete, high-performance method for uncertainty-aware HSI reconstruction.
  arXiv  Detail & Related papers  (2025-11-23T14:43:51Z)
• GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution [19.608052570649303]
  We propose a novel framework for reconstructing hyperspectral images at 4-times super-resolution.<n>A wavelet-based encoder-decoder is introduced that efficiently compresses HSIs into a latent space while preserving spectral-spatial information.<n>Our model demonstrated state-of-the-art results across multiple dimensions, including fidelity, spectral accuracy, visual realism, and clarity.
  arXiv  Detail & Related papers  (2025-11-10T13:44:16Z)
• HAD: Hierarchical Asymmetric Distillation to Bridge Spatio-Temporal Gaps in Event-Based Object Tracking [80.07224739976911]
  Event cameras offer exceptional temporal resolution and a range (modal)<n> RGB cameras excel at capturing rich texture with high resolution, whereas event cameras offer exceptional temporal resolution and a range (modal)
  arXiv  Detail & Related papers  (2025-10-22T13:15:13Z)
• D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens [80.75893450536577]
  We propose D2C, a novel two-stage method to enhance model generation capacity.<n>In the first stage, the discrete-valued tokens representing coarse-grained image features are sampled by employing a small discrete-valued generator.<n>In the second stage, the continuous-valued tokens representing fine-grained image features are learned conditioned on the discrete token sequence.
  arXiv  Detail & Related papers  (2025-03-21T13:58:49Z)
• Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model [1.9746060146273674]
  This paper proposes Txt2HSI-LDM(VAE), a novel language-informed hyperspectral image synthesis method.<n>To address the high-dimensionality of hyperspectral data, a universal variational autoencoder (VAE) is designed to map the data into a low-dimensional latent space.<n>VAE decodes HSI from latent space generated by the diffusion model with the language conditions as input.
  arXiv  Detail & Related papers  (2025-02-27T02:35:49Z)
• Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective [52.778766190479374]
  Latent-based image generative models have achieved notable success in image generation tasks. Despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. We propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling.
  arXiv  Detail & Related papers  (2024-10-16T12:13:17Z)
• Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis [62.57727062920458]
  We present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL.<n>We leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers.<n>Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images.
  arXiv  Detail & Related papers  (2024-10-10T17:59:17Z)
• Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network [29.6360974619655]
  Group-Autoencoder (GAE) framework encodes high-dimensional hyperspectral data into low-dimensional latent space. DMGASR construct highly effective HSI SR model (DMGASR) Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically.
  arXiv  Detail & Related papers  (2024-02-27T07:57:28Z)
• Physics-Inspired Degradation Models for Hyperspectral Image Fusion [61.743696362028246]
  Most fusion methods solely focus on the fusion algorithm itself and overlook the degradation models. We propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI. Our proposed PIDM can boost the fusion performance of existing fusion methods in practical scenarios.
  arXiv  Detail & Related papers  (2024-02-04T09:07:28Z)
• A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution [14.405562058304074]
  Fusion-based hyperspectral image (HSI) super-resolution aims to produce a high-spatial-resolution HSI by fusing a low-spatial-resolution HSI and a high-spatial-resolution multispectral image. Motivated by the success of diffusion models, we propose a novel spectral diffusion prior for fusion-based HSI super-resolution.
  arXiv  Detail & Related papers  (2023-11-15T13:40:58Z)
• Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis [62.07413805483241]
  Steered Diffusion is a framework for zero-shot conditional image generation using a diffusion model trained for unconditional generation. We present experiments using steered diffusion on several tasks including inpainting, colorization, text-guided semantic editing, and image super-resolution.
  arXiv  Detail & Related papers  (2023-09-30T02:03:22Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion [7.06521373423708]
  Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation. We introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation. Our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks.
  arXiv  Detail & Related papers  (2023-04-10T12:28:27Z)
• Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance [95.12230117950232]
  We show that a common latent space emerges from two diffusion models trained independently on related domains. Applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors.
  arXiv  Detail & Related papers  (2022-10-11T15:53:52Z)
• Auto-regressive Image Synthesis with Integrated Quantization [55.51231796778219]
  This paper presents a versatile framework for conditional image generation. It incorporates the inductive bias of CNNs and powerful sequence modeling of auto-regression. Our method achieves superior diverse image generation performance as compared with the state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-21T22:19:17Z)
• Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation [19.156223720614186]
  The integration of Vector Quantised Variational AutoEncoder with autoregressive models as generation part has yielded high-quality results on image generation. We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context.
  arXiv  Detail & Related papers  (2021-12-03T09:09:34Z)
• A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution [3.1023808510465627]
  generative adversarial network (GAN) has proven to be an effective deep learning framework for image super-resolution. To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN) LE-GAN can map the generated spectral-spatial features from the image space to the latent space and produce a coupling component to regularise the generated samples.
  arXiv  Detail & Related papers  (2021-11-16T18:40:19Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.