GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB Images

• URL: http://arxiv.org/abs/2405.07777v2
• Date: Wed, 09 Oct 2024 11:29:10 GMT
• Title: GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB Images
• Authors: Xinying Wang, Zhixiong Huang, Sifan Zhang, Jiawen Zhu, Paolo Gamba, Lin Feng,
• Abstract summary: GMSR-Net is a lightweight model characterized by a global receptive field and linear computational complexity. It achieves state-of-the-art performance while markedly reducing the number of parameters and computational burdens. Compared to existing approaches, GMSR-Net slashes parameters and FLOPS by substantial margins of 10 times and 20 times, respectively.
• Score: 6.437310105552873
• License:
• Abstract: Mainstream approaches to spectral reconstruction (SR) primarily focus on designing Convolution- and Transformer-based architectures. However, CNN methods often face challenges in handling long-range dependencies, whereas Transformers are constrained by computational efficiency limitations. Recent breakthroughs in state-space model (e.g., Mamba) has attracted significant attention due to its near-linear computational efficiency and superior performance, prompting our investigation into its potential for SR problem. To this end, we propose the Gradient-guided Mamba for Spectral Reconstruction from RGB Images, dubbed GMSR-Net. GMSR-Net is a lightweight model characterized by a global receptive field and linear computational complexity. Its core comprises multiple stacked Gradient Mamba (GM) blocks, each featuring a tri-branch structure. In addition to benefiting from efficient global feature representation by Mamba block, we further innovatively introduce spatial gradient attention and spectral gradient attention to guide the reconstruction of spatial and spectral cues. GMSR-Net demonstrates a significant accuracy-efficiency trade-off, achieving state-of-the-art performance while markedly reducing the number of parameters and computational burdens. Compared to existing approaches, GMSR-Net slashes parameters and FLOPS by substantial margins of 10 times and 20 times, respectively. Code is available at https://github.com/wxy11-27/GMSR.

Related papers

• CDXFormer: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory [3.119836924407993]
  We propose CDXFormer, with a core component that is a powerful XLSTM-based spatial enhancement layer. We introduce a scale-specific Feature Enhancer layer, incorporating a Cross-Temporal Global Perceptron customized for semantic-accurate deep features. We also propose a Cross-Scale Interactive Fusion module to progressively interact global change representations with responses.
  arXiv  Detail & Related papers  (2024-11-12T15:22:14Z)
• Bidirectional Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking [51.28485682954006]
  We propose a pure Mamba-based framework (MambaVT) to fully exploit intrinsic-temporal contextual modeling for robust visible-thermal tracking. Specifically, we devise the long-range cross-frame integration component to globally adapt to target appearance variations. Experiments show the significant potential of vision Mamba for RGB-T tracking, with MambaVT achieving state-of-the-art performance on four mainstream benchmarks.
  arXiv  Detail & Related papers  (2024-08-15T02:29:00Z)
• Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution [49.902047563260496]
  We develop the first attempt to integrate the Vision State Space Model (Mamba) for remote sensing image (RSI) super-resolution. To achieve better SR reconstruction, building upon Mamba, we devise a Frequency-assisted Mamba framework, dubbed FMSR. Our FMSR features a multi-level fusion architecture equipped with the Frequency Selection Module (FSM), Vision State Space Module (VSSM), and Hybrid Gate Module (HGM)
  arXiv  Detail & Related papers  (2024-05-08T11:09:24Z)
• DVMSR: Distillated Vision Mamba for Efficient Super-Resolution [7.551130027327461]
  We propose DVMSR, a novel lightweight Image SR network that incorporates Vision Mamba and a distillation strategy. Our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters.
  arXiv  Detail & Related papers  (2024-05-05T17:34:38Z)
• Binarized Spectral Compressive Imaging [59.18636040850608]
  Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. We propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet) BiSRNet is derived by using the proposed techniques to binarize the base model.
  arXiv  Detail & Related papers  (2023-05-17T15:36:08Z)
• Large-scale Global Low-rank Optimization for Computational Compressed Imaging [8.594666859332124]
  We present the global low-rank (GLR) optimization technique, realizing highly-efficient large-scale reconstruction with global self-similarity. Inspired by the self-attention mechanism in deep learning, GLR extracts image patches by feature detection instead of conventional uniform selection. We experimentally demonstrate GLR's effectiveness on temporal, frequency, and spectral dimensions.
  arXiv  Detail & Related papers  (2023-01-08T14:12:51Z)
• MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction [148.26195175240923]
  We propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++) for efficient spectral reconstruction. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place.
  arXiv  Detail & Related papers  (2022-04-17T02:39:32Z)
• GhostSR: Learning Ghost Features for Efficient Image Super-Resolution [49.393251361038025]
  Single image super-resolution (SISR) system based on convolutional neural networks (CNNs) achieves fancy performance while requires huge computational costs. We propose to use shift operation to generate the redundant features (i.e., Ghost features) of SISR models. We show that both the non-compact and lightweight SISR models embedded in our proposed module can achieve comparable performance to that of their baselines.
  arXiv  Detail & Related papers  (2021-01-21T10:09:47Z)

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.