Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening

• URL: http://arxiv.org/abs/2409.02675v1
• Date: Wed, 4 Sep 2024 13:05:00 GMT
• Title: Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening
• Authors: Ivan Pereira-Sánchez, Eloi Sans, Julia Navarro, Joan Duran,
• Abstract summary: Unfolding fusion methods integrate the powerful representation capabilities of deep learning with the robustness of model-based approaches. In this paper, we propose a model-based deep unfolded method for satellite image fusion. Experimental results on PRISMA, Quickbird, and WorldView2 datasets demonstrate the superior performance of our method.
• Score: 2.874893537471256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The objective of pansharpening and hypersharpening is to accurately combine a high-resolution panchromatic (PAN) image with a low-resolution multispectral (MS) or hyperspectral (HS) image, respectively. Unfolding fusion methods integrate the powerful representation capabilities of deep learning with the robustness of model-based approaches. These techniques involve unrolling the steps of the optimization scheme derived from the minimization of an energy into a deep learning framework, resulting in efficient and highly interpretable architectures. In this paper, we propose a model-based deep unfolded method for satellite image fusion. Our approach is based on a variational formulation that incorporates the classic observation model for MS/HS data, a high-frequency injection constraint based on the PAN image, and an arbitrary convex prior. For the unfolding stage, we introduce upsampling and downsampling layers that use geometric information encoded in the PAN image through residual networks. The backbone of our method is a multi-head attention residual network (MARNet), which replaces the proximity operator in the optimization scheme and combines multiple head attentions with residual learning to exploit image self-similarities via nonlocal operators defined in terms of patches. Additionally, we incorporate a post-processing module based on the MARNet architecture to further enhance the quality of the fused images. Experimental results on PRISMA, Quickbird, and WorldView2 datasets demonstrate the superior performance of our method and its ability to generalize across different sensor configurations and varying spatial and spectral resolutions. The source code will be available at https://github.com/TAMI-UIB/MARNet.

Related papers

• A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding [76.44979557843367]
  We propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior. We introduce a Multi-view Disparity Attention (MDA) module to aggregate long-range context information. We explicitly estimate the quality of the current pixel corresponding to sampled points on the epipolar line of the source image.
  arXiv  Detail & Related papers  (2024-11-04T08:50:16Z)
• Variational Zero-shot Multispectral Pansharpening [43.881891055500496]
  Pansharpening aims to generate a high spatial resolution multispectral image (HRMS) by fusing a low spatial resolution multispectral image (LRMS) and a panchromatic image (PAN) Existing deep learning-based methods are unsuitable since they rely on many training pairs. We propose a zero-shot pansharpening method by introducing a neural network into the optimization objective.
  arXiv  Detail & Related papers  (2024-07-09T07:59:34Z)
• Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks [53.67497327319569]
  We introduce a novel neural rendering technique to solve image-to-3D from a single view. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Our experiments show the advantages of our proposed approach with consistent results and rapid generation.
  arXiv  Detail & Related papers  (2023-12-24T08:42:37Z)
• Improving Pixel-based MIM by Reducing Wasted Modeling Capability [77.99468514275185]
  We propose a new method that explicitly utilizes low-level features from shallow layers to aid pixel reconstruction. To the best of our knowledge, we are the first to systematically investigate multi-level feature fusion for isotropic architectures. Our method yields significant performance gains, such as 1.2% on fine-tuning, 2.8% on linear probing, and 2.6% on semantic segmentation.
  arXiv  Detail & Related papers  (2023-08-01T03:44:56Z)
• Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening [13.593522290577512]
  Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency.
  arXiv  Detail & Related papers  (2023-04-28T03:34:36Z)
• PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening [50.943080184828524]
  We propose a novel two-step model for pan-sharpening that sharpens the MS image through the progressive compensation of the spatial and spectral information. The whole model is composed of triple GANs, and based on the specific architecture, a joint compensation loss function is designed to enable the triple GANs to be trained simultaneously.
  arXiv  Detail & Related papers  (2022-07-29T03:09:21Z)
• Proximal PanNet: A Model-Based Deep Network for Pansharpening [11.695233311615498]
  We propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. We unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2022-02-12T15:49:13Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution [25.878793557013207]
  This paper focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image. Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples. We make the first attempt to design a model inspired deep network for HSI super-resolution in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-10-22T05:15:16Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.