The paper introduces a spatial-frequency-aware implicit neural representation that fuses a Multi‑Layer Perceptron (MLP) branch with a Kolmogorov‑Arnold network (KAN) branch. The MLP captures low‑frequency, smooth structures while the KAN captures high‑frequency, localized details, and their outputs are combined using discrete wavelet transform (DWT) and inverse DWT. A wavelet‑domain band‑separation regularization encourages complementary frequency behavior, and experiments on 1D–4D signals show improved reconstruction fidelity.
By Wen Yan, Ligen Shi, Jun Qiu, Haimiao Zhang, Lina Wu, Chang Liu
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.
By Jiabao Wang, Wenjian Liu, Yaoming Cai, Gengyu Zhang, Boyan Zhao, Zijia Zhang, Yao Ding, Xiaobo Liu
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.
By Wenbin Zou, Yawen Cui, Yi Wang, Lap-Pui Chau, Liang Chen, Jinshan Pan, Huiping Zhuang, Guanbin Li
arXiv:2609.39004v1 Announce Type: new
Abstract: Multimodal image fusion (MMIF) aims to integrate complementary information from different modalities into a high-quality fused image and support downst...
By Zeyu Wang, Jiayu Wang, Haiyu Song, Haoran Duan
The paper introduces PI-AMFM, a permutation‑invariant neural framework that learns to decompose biomedical signals into variable‑cardinality amplitude‑ and frequency‑modulated (AM‑FM) modes. It combines a multiscale temporal encoder, a Mamba backbone, and component‑presence estimation, using Hungarian matching during training to handle unknown numbers of components. Experiments on synthetic AM‑FM signals and photoplethysmographic recordings show that PI‑AMFM outperforms existing methods in decomposition accuracy and successfully captures cardiac and respiratory dynamics without prior knowledge of mode count.
By Youngsun Kong, Ki H. Chon
arXiv:2511.18028v2 Announce Type: replace
Abstract: Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches main...
By Chenyu Li, Danfeng Hong, Bing Zhang, Zhaojie Pan, Naoto Yokoya, Jocelyn Chanussot
arXiv:2609.25429v1 Announce Type: new
Abstract: In this paper, we introduce the Directional Total Variation-Regularized Implicit Neural Representation (DTV-INR), an advanced variational paradigm that...
By Mahmoud Saeedi Kelishami
arXiv:2608.17623v2 Announce Type: replace
Abstract: Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Opti...
By Cheng Cheng, Jin Hong
arXiv:2607.05176v3 Announce Type: replace
Abstract: Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by r...
By Aiwen Liu, Chengguang Zhu, Gang Wang, Dandan Zhu, Haodong Lin, Yan Wang, Huiyu Zhou, Zhengyi Pan
The paper investigates when frequency decomposition aids Physics-Informed Neural Networks (PINNs) by introducing a dual‑branch, spectrally‑gated architecture (DBSG‑PINN) that separates low‑ and high‑frequency components. Experiments on five one‑dimensional PDE benchmarks show that frequency decomposition significantly reduces error—up to 59.2% on a multimodal wave problem—when the target solution is spectrally complex, but offers little improvement on smoother problems and can even worsen performance on a simple 1D wave benchmark. The adaptive gate’s effectiveness scales with the spectral richness of the solution, suggesting it exploits frequency structure rather than adding noise.
By Shubham Rai
arXiv:2510.04382v3 Announce Type: replace-cross
Abstract: Even though more than 30 years have passed since the seminal Rudin--Osher--Fatemi (ROF) paper on total variation (TV) denoising, it remains r...
By Wojciech G\'orny, Micha{\l} {\L}asica, Alexandros Matsoukas
The paper introduces FReSH-IR, a lightweight all-in-one weather restoration method that decomposes feature representations into high- and low-frequency components across a hierarchical encoder-decoder. By integrating spectral decomposition with Fourier-based skip connections, it captures complementary frequency information while preserving spatial detail. The approach achieves comparable restoration quality to transformer-based models while using 80% fewer parameters and operations, making it efficient for high-resolution images and constrained-resource systems.
By Paula Garrido-Mellado, Daniel Feijoo, Yuning Cui, Alvaro Garcia, Marcos V. Conde