Bi^2MAC: Bimodal Bi-Adaptive Mask-Aware Convolution for Remote Sensing Pansharpening

• URL: http://arxiv.org/abs/2512.08331v1
• Date: Tue, 09 Dec 2025 08:00:43 GMT
• Title: Bi^2MAC: Bimodal Bi-Adaptive Mask-Aware Convolution for Remote Sensing Pansharpening
• Abstract summary: We introduce Bimodal Bi-Adaptive Mask-Aware Convolution (Bi2MAC)<n>It exploits information from different types of regions while intelligently allocating computational resources.<n>Bi2MAC achieves state-of-the-art (SOTA) performance while requiring substantially lower training time and parameter counts.
• Score: 18.789955600742168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pansharpening aims to fuse a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to generate a high-resolution multispectral image (HRMS). Conventional deep learning-based methods are inherently limited in their ability to adapt to regional heterogeneity within feature representations. Although various adaptive convolution methods have been proposed to address this limitation, they often suffer from excessive computational costs and a limited ability to capture heterogeneous regions in remote sensing images effectively. To overcome these challenges, we propose Bimodal Bi-Adaptive Mask-Aware Convolution (Bi^2MAC), which effectively exploits information from different types of regions while intelligently allocating computational resources. Specifically, we design a lightweight module to generate both soft and hard masks, which are used to modulate the input features preliminarily and to guide different types of regions into separate processing branches, respectively. Redundant features are directed to a compact branch for low-cost global processing. In contrast, heterogeneous features are routed to a focused branch that invests more computational resources for fine-grained modeling. Extensive experiments on multiple benchmark datasets demonstrate that Bi^2MAC achieves state-of-the-art (SOTA) performance while requiring substantially lower training time and parameter counts, and the minimal computational cost among adaptive convolution models.

Related papers

• Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion [25.15016853820625]
  Deep learning models have achieved excellent performance, but they often come with high computational complexity.<n>In this paper, we explore the feasibility of applying the binary neural network (BNN) to pan-sharpening.<n>A series of S2B-Conv form a brand-new binary network for pan-sharpening, dubbed as S2BNet.
  arXiv  Detail & Related papers  (2025-09-27T14:10:51Z)
• RegistrationMamba: A Mamba-based Registration Framework Integrating Multi-Expert Feature Learning for Cross-Modal Remote Sensing Images [39.5745769925092]
  Cross-modal remote sensing image (CRSI) registration is critical for multi-modal image applications.<n>Existing methods mainly adopt convolutional neural networks (CNNs) or Transformer architectures to extract discriminative features for registration.<n>This paper proposes RegistrationMamba, a novel Mamba architecture based on state space models (SSMs) integrating multi-expert feature learning.
  arXiv  Detail & Related papers  (2025-07-06T13:59:51Z)
• QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution [54.67891514843853]
  We propose the Quadtree Diffusion Model (QDM), a region-adaptive diffusion framework.<n>By guiding the diffusion with a quadtree derived from the low-quality input, QDM identifies key regions-represented by leaf nodes-where fine detail is essential.<n> Experiments demonstrate QDM's effectiveness in high-resolution SR tasks across diverse image types, particularly in medical imaging.
  arXiv  Detail & Related papers  (2025-03-15T06:50:30Z)
• Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution [52.55429225242423]
  We propose a novel framework for Burst Image Super-Resolution (BISR), featuring an equivariant convolution-based alignment.<n>This enables the alignment transformation to be learned via explicit supervision in the image domain and easily applied in the feature domain.<n>Experiments on BISR benchmarks show the superior performance of our approach in both quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2025-03-11T11:13:10Z)
• Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation [13.610095493539397]
  We propose a guided and multi-scale feature aggregated network for IFL.<n>In order to learn the noise feature under different types of forgery, we develop an effective noise extraction module.<n>Then, we design a Feature Aggregation Module (FAM) that uses dynamic convolution to adaptively aggregate RGB and noise features over multiple scales.<n>Finally, we propose an Atrous Residual Pyramid Module (ARPM) to enhance features representation and capture both global and local features.
  arXiv  Detail & Related papers  (2024-11-17T11:50:09Z)
• Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation [54.96563068182733]
  We propose Modality Adaptation with text-to-image Diffusion Models (MADM) for semantic segmentation task. MADM utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model's cross-modality capabilities. We show that MADM achieves state-of-the-art adaptation performance across various modality tasks, including images to depth, infrared, and event modalities.
  arXiv  Detail & Related papers  (2024-10-29T03:49:40Z)
• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia.<n>Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored.<n>We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization [52.87635234206178]
  This paper proposes a new framework, namely MoNFAP, specifically tailored for multi-face manipulation detection and localization. The framework incorporates two novel modules: the Forgery-aware Unified Predictor (FUP) Module and the Mixture-of-Noises Module (MNM)
  arXiv  Detail & Related papers  (2024-08-05T08:35:59Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• 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)

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.