A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution

• URL: http://arxiv.org/abs/2505.03431v1
• Date: Tue, 06 May 2025 11:15:59 GMT
• Title: A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution
• Authors: Usman Muhammad, Jorma Laaksonen,
• Abstract summary: We propose a single-image super-resolution model called the Fusion-Guided Inception Network (FGIN)<n>Specifically, we first employ a spectral-spatial fusion module to effectively integrate spectral and spatial information.<n>An Inception-like hierarchical feature extraction strategy is used to capture multiscale spatial dependencies.<n>To further enhance reconstruction quality, we incorporate an optimized upsampling module that combines bilinear with depthwise separable convolutions.
• Score: 4.487807378174191
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fusion of low-spatial-resolution hyperspectral images (HSIs) with high-spatial-resolution conventional images (e.g., panchromatic or RGB) has played a significant role in recent advancements in HSI super-resolution. However, this fusion process relies on the availability of precise alignment between image pairs, which is often challenging in real-world scenarios. To mitigate this limitation, we propose a single-image super-resolution model called the Fusion-Guided Inception Network (FGIN). Specifically, we first employ a spectral-spatial fusion module to effectively integrate spectral and spatial information at an early stage. Next, an Inception-like hierarchical feature extraction strategy is used to capture multiscale spatial dependencies, followed by a dedicated multi-scale fusion block. To further enhance reconstruction quality, we incorporate an optimized upsampling module that combines bilinear interpolation with depthwise separable convolutions. Experimental evaluations on two publicly available hyperspectral datasets demonstrate the competitive performance of our method.

Related papers

• Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model [17.94165288907444]
  Hyperspectral and multispectral image (HSI-MSI) fusion involves combining a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral image (HR-HSI)
  arXiv  Detail & Related papers  (2025-05-17T03:05:13Z)
• Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance [18.045877106333766]
  Hyperspectral images super-resolution aims to improve the spatial resolution, yet its performance is often limited at high-resolution ratios.<n>We introduce a framework for unaligned reference RGB guided HSI SR to address the issues of inaccurate alignment and poor interactivity.<n>Our method outperforms state-of-the-art approaches on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2025-05-04T13:29:31Z)
• 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)
• SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution [73.46167948298041]
  We propose a neural implicit model that represents an image as a function of both continuous pixel coordinates in the spatial domain and continuous wavelengths in the spectral domain. We show that SSIF generalizes well to both unseen spatial resolutions and spectral resolutions. It can generate high-resolution images that improve the performance of downstream tasks by 1.7%-7%.
  arXiv  Detail & Related papers  (2023-09-30T15:23:30Z)
• 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)
• Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency [21.233354336608205]
  We propose an unsupervised HSI and MSI fusion model based on the cycle consistency, called CycFusion. The CycFusion learns the domain transformation between low spatial resolution HSI (LrHSI) and high spatial resolution MSI (HrMSI) Experiments conducted on several datasets show that our proposed model outperforms all compared unsupervised fusion methods.
  arXiv  Detail & Related papers  (2023-07-07T06:47:15Z)
• Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion [67.35540259040806]
  We propose a subpixel-level HS super-resolution framework by devising a novel decoupled-and-coupled network, called DCNet. As the name suggests, DC-Net first decouples the input into common (or cross-sensor) and sensor-specific components. We append a self-supervised learning module behind the CSU net by guaranteeing the material consistency to enhance the detailed appearances of the restored HS product.
  arXiv  Detail & Related papers  (2022-05-07T23:40:36Z)
• Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution [9.022005574190182]
  We design a network based on the transformer for fusing the low-resolution hyperspectral images and high-resolution multispectral images. Considering the LR-HSIs hold the main spectral structure, the network focuses on the spatial detail estimation. Various experiments and quality indexes show our approach's superiority compared with other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-05T14:00:34Z)
• Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses [67.01164492518481]
  This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses. We propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input. Our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission.
  arXiv  Detail & Related papers  (2021-02-14T06:44:47Z)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)
• 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.