Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution

• URL: http://arxiv.org/abs/2502.09654v2
• Date: Thu, 03 Apr 2025 02:27:53 GMT
• Title: Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution
• Abstract summary: We introduce a Mixture of Experts (MoE) model and design a set of heterogeneous experts.<n>These experts are organized into multiple expert groups, where experts within each group are homogeneous while being heterogeneous across groups.<n> Experiments conducted on the UCMerced and AID datasets demonstrate that our proposed method achieves superior SR reconstruction accuracy.
• Score: 23.383837540690823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing image super-resolution (SR) aims to reconstruct high-resolution remote sensing images from low-resolution inputs, thereby addressing limitations imposed by sensors and imaging conditions. However, the inherent characteristics of remote sensing images, including diverse ground object types and complex details, pose significant challenges to achieving high-quality reconstruction. Existing methods typically employ a uniform structure to process various types of ground objects without distinction, making it difficult to adapt to the complex characteristics of remote sensing images. To address this issue, we introduce a Mixture of Experts (MoE) model and design a set of heterogeneous experts. These experts are organized into multiple expert groups, where experts within each group are homogeneous while being heterogeneous across groups. This design ensures that specialized activation parameters can be employed to handle the diverse and intricate details of ground objects effectively. To better accommodate the heterogeneous experts, we propose a multi-level feature aggregation strategy to guide the routing process. Additionally, we develop a dual-routing mechanism to adaptively select the optimal expert for each pixel. Experiments conducted on the UCMerced and AID datasets demonstrate that our proposed method achieves superior SR reconstruction accuracy compared to state-of-the-art methods. The code will be available at https://github.com/Mr-Bamboo/MFG-HMoE.

Related papers

• FaceMoE: Mixture of Experts for Low-Resolution Face Recognition [54.04077186145988]
  We propose FaceMoE, an effective adaptation of Mixture of Experts (MoE) transfomer architecture for low-resolution face-recognition.<n>Specifically, we introduce multiple specialized feed-forward network (FFN) experts and incorporate a top-k router, which dynamically assigns tokens to appropriate experts.<n>This design emergently promotes specialization across experts for different semantic regions of the face, which enables FaceMoE to perform resolution-aware feature extraction.
  arXiv  Detail & Related papers  (2026-06-30T17:59:57Z)
• SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution [99.56535031145211]
  State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR)<n>We propose textbfSP-MoMamba, a superpixel-driven mixture of state space experts for content-aware SR.<n>Our core idea is to transform the traditional rigid scanning into a textbfsemantic-level interaction by treating superpixels as fundamental units.
  arXiv  Detail & Related papers  (2026-05-25T14:19:59Z)
• Can Large Pretrained Depth Estimation Models Help With Image Dehazing? [35.4696172315888]
  Image dehazing remains a challenging problem due to the spatially varying nature of haze in real-world scenes.<n>We propose a plug-and-play RGB-D fusion module that seamlessly integrates with diverse dehazing architectures.
  arXiv  Detail & Related papers  (2025-08-01T15:14:45Z)
• UniUIR: Considering Underwater Image Restoration as An All-in-One Learner [49.35128836844725]
  We propose a Universal Underwater Image Restoration method, termed as UniUIR.<n>To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module.<n>This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts.
  arXiv  Detail & Related papers  (2025-01-22T16:10:42Z)
• Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior [13.148815217684277]
  Large scale factor super-resolution (SR) algorithms are vital for maximizing the utilization of low-resolution (LR) satellite data captured from orbit. Existing methods confront challenges in recovering SR images with clear textures and correct ground objects. We introduce a novel framework, the Semantic Guided Diffusion Model (SGDM), designed for large scale factor remote sensing image super-resolution.
  arXiv  Detail & Related papers  (2024-05-11T16:06:16Z)
• Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion [60.221404321514086]
  Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. This paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions.
  arXiv  Detail & Related papers  (2023-09-03T08:07:26Z)
• 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)
• Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution [55.52779466954026]
  Multi-contrast super-resolution (SR) reconstruction is promising to yield SR images with higher quality. Existing methods lack effective mechanisms to match and fuse these features for better reconstruction. We propose a novel network to address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques.
  arXiv  Detail & Related papers  (2022-03-26T01:42:59Z)
• High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion [84.24973877109181]
  We propose a novel attention-based hierarchical multi-modal fusion network for guided DSR. We show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
  arXiv  Detail & Related papers  (2021-04-04T03:28:33Z)
• Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors [21.264746234523678]
  Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors. In this paper, we attempt to leverage complementary information from a low-cost channel (visible/depth) to boost image quality of an expensive channel (thermal) using fewer parameters.
  arXiv  Detail & Related papers  (2020-12-07T02:15:28Z)
• Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics [72.9038524082252]
  We propose a compact single-shot monocular hyperspectral-depth (HS-D) imaging method. Our method uses a diffractive optical element (DOE), the point spread function of which changes with respect to both depth and spectrum. To facilitate learning the DOE, we present a first HS-D dataset by building a benchtop HS-D imager.
  arXiv  Detail & Related papers  (2020-09-01T14:19:35Z)

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.