Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images

• URL: http://arxiv.org/abs/2501.01481v1
• Date: Thu, 02 Jan 2025 15:14:40 GMT
• Title: Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images
• Authors: Fuxiang Feng, Runmin Cong, Shoushui Wei, Yipeng Zhang, Jun Li, Sam Kwong, Wei Zhang,
• Abstract summary: We propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module.
• Score: 64.80875911446937
• License:
• Abstract: Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance.

Related papers

• HSRMamba: Contextual Spatial-Spectral State Space Model for Single Hyperspectral Super-Resolution [41.93421212397078]
  Mamba has demonstrated exceptional performance in visual tasks due to its powerful global modeling capabilities and linear computational complexity. In HSISR, Mamba faces challenges as transforming images into 1D sequences neglects the spatial-spectral structural relationships between locally adjacent pixels. We propose HSRMamba, a contextual spatial-spectral modeling state space model for HSISR, to address these issues both locally and globally.
  arXiv  Detail & Related papers  (2025-01-30T17:10:53Z)
• EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution [15.459253235077375]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to improve the resolution of a single input low-resolution HSI. Due to the bottleneck of data scarcity, the development of single-HSI-SR lags far behind that of RGB natural images.
  arXiv  Detail & Related papers  (2024-09-06T06:46:01Z)
• Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence [26.1694389791047]
  Spectral super-resolution aims to recover hyperspectral image (HSI) from easily obtainable RGB image. Two types of bottlenecks in existing Transformers limit performance improvement and practical applications. We propose a novel Exhaustive Correlation Transformer (ECT) for spectral super-resolution.
  arXiv  Detail & Related papers  (2023-12-20T08:30:07Z)
• 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)
• Recursive Generalization Transformer for Image Super-Resolution [108.67898547357127]
  We propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images. We combine the RG-SA with local self-attention to enhance the exploitation of the global context. Our RGT outperforms recent state-of-the-art methods quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2023-03-11T10:44:44Z)
• Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution [0.0]
  Implicit Neural Representations (INRs) are making strides as a novel and effective representation. We propose a novel HSI reconstruction model based on INR which represents HSI by a continuous function mapping a spatial coordinate to its corresponding spectral radiance values.
  arXiv  Detail & Related papers  (2021-12-20T14:07:54Z)
• 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)
• Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction [127.20208645280438]
  Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. Mask-guided Spectral-wise Transformer (MST) proposes a novel framework for HSI reconstruction.
  arXiv  Detail & Related papers  (2021-11-15T16:59:48Z)
• Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration [66.68541690283068]
  We propose a unified paradigm combining the spatial and spectral properties for hyperspectral image restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity. Experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority.
  arXiv  Detail & Related papers  (2020-10-24T15:53:56Z)
• Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution [79.97180849505294]
  We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI. Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
  arXiv  Detail & Related papers  (2020-07-10T08:08:20Z)

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.