Deform-Mamba Network for MRI Super-Resolution

• URL: http://arxiv.org/abs/2407.05969v1
• Date: Mon, 8 Jul 2024 14:07:26 GMT
• Title: Deform-Mamba Network for MRI Super-Resolution
• Authors: Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan,
• Abstract summary: We propose a new architecture, called Deform-Mamba, for MR image super-resolution. We develop a Deform-Mamba encoder which is composed of two branches, modulated deform block and vision Mamba block. Thanks to the extracted features of the encoder, which include content-adaptive local and efficient global information, the vision Mamba decoder finally generates high-quality MR images.
• Score: 7.97504951029884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a new architecture, called Deform-Mamba, for MR image super-resolution. Unlike conventional CNN or Transformer-based super-resolution approaches which encounter challenges related to the local respective field or heavy computational cost, our approach aims to effectively explore the local and global information of images. Specifically, we develop a Deform-Mamba encoder which is composed of two branches, modulated deform block and vision Mamba block. We also design a multi-view context module in the bottleneck layer to explore the multi-view contextual content. Thanks to the extracted features of the encoder, which include content-adaptive local and efficient global information, the vision Mamba decoder finally generates high-quality MR images. Moreover, we introduce a contrastive edge loss to promote the reconstruction of edge and contrast related content. Quantitative and qualitative experimental results indicate that our approach on IXI and fastMRI datasets achieves competitive performance.

Related papers

• Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution [6.646855815619148]
  We propose a global and local Mamba network (GLMamba) for multi-modality medical image super-resolution. The global Mamba branch captures long-range relationships in low-resolution inputs, and the local Mamba branch focuses more on short-range details in high-resolution reference images. A modulator is designed to further enhance deformable features in both global and local Mamba blocks.
  arXiv  Detail & Related papers  (2025-04-14T11:14:24Z)
• Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentation [18.060052357308763]
  We propose a prompt-guided CNN-Mamba dual-path UNet, termed PGM-UNet, for medical image segmentation. We introduce a prompt-guided residual Mamba module that adaptively extracts dynamic visual prompts from the original input data. We also design a local-global information fusion network, comprising a local information extraction module, a prompt-guided residual Mamba module, and a multi-focus attention fusion module.
  arXiv  Detail & Related papers  (2025-03-25T12:12:07Z)
• MatIR: A Hybrid Mamba-Transformer Image Restoration Model [95.17418386046054]
  We propose a Mamba-Transformer hybrid image restoration model called MatIR. MatIR cross-cycles the blocks of the Transformer layer and the Mamba layer to extract features. In the Mamba module, we introduce the Image Inpainting State Space (IRSS) module, which traverses along four scan paths.
  arXiv  Detail & Related papers  (2025-01-30T14:55:40Z)
• Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion [9.098711843118629]
  This paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet) This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions. By incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features.
  arXiv  Detail & Related papers  (2024-10-08T02:17:38Z)
• Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment [20.902935570581207]
  We introduce a Multimodal Alignment and Reconstruction Network (MARNet) to enhance the model's resistance to visual noise. MARNet includes a cross-modal diffusion reconstruction module for smoothly and stably blending information across different domains. Experiments conducted on two benchmark datasets, Vireo-Food172 and Ingredient-101, demonstrate that MARNet effectively improves the quality of image information extracted by the model.
  arXiv  Detail & Related papers  (2024-07-26T16:30:18Z)
• INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language Model [71.50973774576431]
  We propose a novel MLLM, INF-LLaVA, designed for effective high-resolution image perception. We introduce a Dual-perspective Cropping Module (DCM), which ensures that each sub-image contains continuous details from a local perspective. Second, we introduce Dual-perspective Enhancement Module (DEM) to enable the mutual enhancement of global and local features.
  arXiv  Detail & Related papers  (2024-07-23T06:02:30Z)
• Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution [7.97504951029884]
  We propose a self-prior guided Mamba-UNet network (SMamba-UNet) for medical image super-resolution. Inspired by Mamba, our approach aims to learn the self-prior multi-scale contextual features under Mamba-UNet networks.
  arXiv  Detail & Related papers  (2024-07-08T14:41:53Z)
• IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model [7.842507196763463]
  IRSRMamba is a novel framework integrating wavelet transform feature modulation for multi-scale adaptation. IRSRMamba outperforms state-of-the-art methods in PSNR, SSIM, and perceptual quality. This work establishes Mamba-based architectures as a promising direction for high-fidelity IR image enhancement.
  arXiv  Detail & Related papers  (2024-05-16T07:49:24Z)
• CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration [7.292363114816646]
  We introduce the Channel-Aware U-Shaped Mamba model, which incorporates a dual State Space Model framework into the U-Net architecture. Experiments validate CU-Mamba's superiority over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-04-17T22:02:22Z)
• FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba [19.761723108363796]
  FusionMamba aims to overcome the challenges faced by CNNs and Vision Transformers (ViTs) in computer vision tasks. The framework improves the visual state-space model Mamba by integrating dynamic convolution and channel attention mechanisms. Experiments show that FusionMamba achieves state-of-the-art performance in a variety of multimodal image fusion tasks as well as downstream experiments.
  arXiv  Detail & Related papers  (2024-04-15T06:37:21Z)
• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• Cross-View Hierarchy Network for Stereo Image Super-Resolution [14.574538513341277]
  Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. We propose a novel method, named Cross-View-Hierarchy Network for Stereo Image Super-Resolution (CVHSSR) CVHSSR achieves the best stereo image super-resolution performance than other state-of-the-art methods while using fewer parameters.
  arXiv  Detail & Related papers  (2023-04-13T03:11:30Z)
• Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution [64.25751738088015]
  Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks. Transformers that need to incorporate contextual information to extract features dynamically are neglected. We propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer.
  arXiv  Detail & Related papers  (2022-07-06T16:32:29Z)
• CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training [112.96224800952724]
  We propose cascaded modulation GAN (CM-GAN) to generate plausible image structures when dealing with large holes in complex images. In each decoder block, global modulation is first applied to perform coarse semantic-aware synthesis structure, then spatial modulation is applied on the output of global modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios.
  arXiv  Detail & Related papers  (2022-03-22T16:13:27Z)
• 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)
• 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.