arXiv Computer Vision
MambaX: Image Super-Resolution with State Predictive Control
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arXiv Computer Vision
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Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution
The paper introduces TSR-ITNR, a two‑stage, self‑supervised framework for hyperspectral image super‑resolution that fuses high‑resolution multispectral and low‑resolution hyperspectral data. Stage 1 refines an implicit Tucker representation using a low‑rank spatial tensor and spectral basis, enhanced by a pretrained denoiser, to capture fine spatial details and spectral correlations. Stage 2 applies parameter‑free calibration to extract complementary corrections from both observations, preserving geometry and ensuring orthogonal complementarity, leading to superior reconstruction quality demonstrated on benchmark datasets and improved downstream segmentation performance.
arXiv Machine Learning
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MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling
arXiv:2608. 08553v1 Announce Type: cross Abstract: Video super-resolution (VSR) aims to recover high-fidelity high-resolution videos from low-resolution inputs and is central to applications ranging from mobile capture to streaming and archival restoration.
arXiv Machine Learning
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Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems
arXiv:2608. 14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.
arXiv Computer Vision
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SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution
SP-MoMamba introduces a superpixel-driven mixture of state space experts for efficient image super‑resolution. By grouping spatially coherent features into region‑level tokens, the Superpixel‑SSM performs global sequence modeling over compact representations, reducing redundant computation while enabling long‑range structural interaction. The Multi‑Scale Superpixel Mixture of State Space Experts further adapts to varying representation granularities, and a Local Spatial Modulation Expert refines local high‑frequency details, resulting in strong reconstruction performance with a favorable trade‑off among model size, computational cost, and inference efficiency.
arXiv Computer Vision
Sep 14
RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images
RoES is a Rotational Equivariant Selective-frequency fusion network that dynamically separates low- and high-frequency components of infrared-visible images. It uses a trainable rotation-enhanced updater to decouple frequencies, then fuses them with a dual-branch module: a rotation-equivariant Mamba for low-frequency structural dependencies and a polar spectral attention Dual-Fourier block for high-frequency detail refinement. Experiments show RoES outperforms existing methods in fusion quality and downstream object detection, offering a robust multimodal fusion solution.
Hugging Face Trending Papers
Aug 9
MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling
Video super-resolution (VSR) aims to recover high-fidelity high-resolution videos from low-resolution inputs and is central to applications ranging from mobile capture to streaming and archival restoration. Existing approaches trade off among local-detail fidelity, long-range spatio-temporal modeling, perceptual realism, and efficiency: convolutional alignment techniques preserve local structure but suffer when motion is large or degradations are complex; transformer-based methods capture long-range dependencies yet require architectural or algorithmic adaptations to remain computationally feasible; and recent latent or diffusion-based generators synthesize rich texture but require specialized temporal constraints to maintain coherence.