Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images

• URL: http://arxiv.org/abs/2302.14239v1
• Date: Tue, 28 Feb 2023 01:44:55 GMT
• Title: Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images
• Authors: Zhongli Fan, Li Zhang, Yuxuan Liu
• Abstract summary: We present an effective method for the matching of multimodal images. Conventional matching methods fail when handling noisy multimodal image pairs with severe scale change, rotation, and nonlinear intensity distortion (NID) We put forward an accurate primary orientation estimation approach to reduce the effect of image rotation at any angle.
• Score: 7.464763738325105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an effective method for the matching of multimodal images. Accurate image matching is the basis of various applications, such as image registration and structure from motion. Conventional matching methods fail when handling noisy multimodal image pairs with severe scale change, rotation, and nonlinear intensity distortion (NID). Toward this need, we introduce an image pyramid strategy to tackle scale change. We put forward an accurate primary orientation estimation approach to reduce the effect of image rotation at any angle. We utilize multi-scale and multi-orientation image filtering results and a feature-to-template matching scheme to ensure effective and accurate matching under large NID. Integrating these improvements significantly increases noise, scale, rotation, and NID invariant capability. Our experimental results confirm the excellent ability to achieve high-quality matches across various multimodal images. The proposed method outperforms the mainstream multimodal image matching methods in qualitative and quantitative evaluations. Our implementation is available at https://github.com/Zhongli-Fan/NISR.

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)
• A Robust Multisource Remote Sensing Image Matching Method Utilizing Attention and Feature Enhancement Against Noise Interference [15.591520484047914]
  We propose a robust multisource remote sensing image matching method utilizing attention and feature enhancement against noise interference. In the first stage, we combine deep convolution with the attention mechanism of transformer to perform dense feature extraction. In the second stage, we introduce an outlier removal network based on a binary classification mechanism.
  arXiv  Detail & Related papers  (2024-10-01T03:35:34Z)
• Self-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization [12.082424048578753]
  We present a self-supervised multi-scale blind image deblurring method to jointly estimate the latent image and the blur kernel. Thanks to the collaborative estimation across multiple scales, our method avoids the computationally intensive coarse-to-fine propagation and additional image deblurring processes.
  arXiv  Detail & Related papers  (2024-09-02T07:08:17Z)
• Cross-Domain Separable Translation Network for Multimodal Image Change Detection [11.25422609271201]
  multimodal change detection (MCD) is particularly critical in the remote sensing community. This paper focuses on addressing the challenges of MCD, especially the difficulty in comparing images from different sensors. A novel unsupervised cross-domain separable translation network (CSTN) is proposed to overcome these limitations.
  arXiv  Detail & Related papers  (2024-07-23T03:56:02Z)
• MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network [65.1004435124796]
  We propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework. Our experiments demonstrate that the proposed MB-RACS method surpasses current leading methods.
  arXiv  Detail & Related papers  (2024-01-19T04:40:20Z)
• Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring [25.36888929483233]
  We propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. We combine the characteristics of real-world trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images.
  arXiv  Detail & Related papers  (2023-12-29T02:59:40Z)
• Deep Diversity-Enhanced Feature Representation of Hyperspectral Images [87.47202258194719]
  We rectify 3D convolution by modifying its topology to enhance the rank upper-bound. We also propose a novel diversity-aware regularization (DA-Reg) term that acts on the feature maps to maximize independence among elements. To demonstrate the superiority of the proposed Re$3$-ConvSet and DA-Reg, we apply them to various HS image processing and analysis tasks.
  arXiv  Detail & Related papers  (2023-01-15T16:19:18Z)
• A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients [7.813406811407584]
  Co-registration of multimodal remote sensing images is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images.
  arXiv  Detail & Related papers  (2022-02-27T12:22:42Z)
• LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation [52.63874513999119]
  Cross-resolution image alignment is a key problem in multiscale giga photography. Existing deep homography methods neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. We propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs.
  arXiv  Detail & Related papers  (2021-06-08T02:51:45Z)
• A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection [93.38607559281601]
  We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions.
  arXiv  Detail & Related papers  (2021-04-29T17:49:48Z)
• TSIT: A Simple and Versatile Framework for Image-to-Image Translation [103.92203013154403]
  We introduce a simple and versatile framework for image-to-image translation. We provide a carefully designed two-stream generative model with newly proposed feature transformations. This allows multi-scale semantic structure information and style representation to be effectively captured and fused by the network. A systematic study compares the proposed method with several state-of-the-art task-specific baselines, verifying its effectiveness in both perceptual quality and quantitative evaluations.
  arXiv  Detail & Related papers  (2020-07-23T15:34:06Z)

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.