UHDRes: Ultra-High-Definition Image Restoration via Dual-Domain Decoupled Spectral Modulation

• URL: http://arxiv.org/abs/2511.05009v1
• Date: Fri, 07 Nov 2025 06:28:30 GMT
• Title: UHDRes: Ultra-High-Definition Image Restoration via Dual-Domain Decoupled Spectral Modulation
• Authors: S. Zhao, W. Lu, B. Wang, T. Wang, K. Zhang, H. Zhao,
• Abstract summary: Ultra-high-definition (UHD) images often suffer from severe degradations such as blur, haze, rain, or low-light conditions.<n>We propose UHDRes, a novel lightweight dual-domain decoupled spectral modulation framework for UHD image restoration.
• Score: 0.07352098890194292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultra-high-definition (UHD) images often suffer from severe degradations such as blur, haze, rain, or low-light conditions, which pose significant challenges for image restoration due to their high resolution and computational demands. In this paper, we propose UHDRes, a novel lightweight dual-domain decoupled spectral modulation framework for UHD image restoration. It explicitly models the amplitude spectrum via lightweight spectrum-domain modulation, while restoring phase implicitly through spatial-domain refinement. We introduce the spatio-spectral fusion mechanism, which first employs a multi-scale context aggregator to extract local and global spatial features, and then performs spectral modulation in a decoupled manner. It explicitly enhances amplitude features in the frequency domain while implicitly restoring phase information through spatial refinement. Additionally, a shared gated feed-forward network is designed to efficiently promote feature interaction through shared-parameter convolutions and adaptive gating mechanisms. Extensive experimental comparisons on five public UHD benchmarks demonstrate that our UHDRes achieves the state-of-the-art restoration performance with only 400K parameters, while significantly reducing inference latency and memory usage. The codes and models are available at https://github.com/Zhao0100/UHDRes.

Related papers

• SR$^{2}$-Net: A General Plug-and-Play Model for Spectral Refinement in Hyperspectral Image Super-Resolution [3.4888894498274747]
  HSI-SR aims to enhance spatial resolution while preserving spectrally faithful and physically plausible characteristics.<n>These methods often neglect spectral consistency across bands, leading to spurious oscillations and physically implausible artifacts.<n>We propose a lightweight plug-and-play, physically priors Spectral Rectification Super-Resolution Network (SR$2$-Net) to address this issue.
  arXiv  Detail & Related papers  (2026-01-29T07:00:00Z)
• Hyperspectral Super-Resolution with Inter-Image Variability via Degradation-based Low-Rank and Residual Fusion Method [2.317803962255901]
  Fusion of hyperspectral image with multispectral image (MSI) provides effective way to enhance spatial resolution of HSI.<n>Due to different acquisition conditions, there may exist spectral variability and spatially localized changes between HSI and MSI.<n>Existing methods typically handle inter-image variability applying direct transformations.<n>We propose a Degradation-based Low-Rank and Residual Fusion (DLRRF) model to address this challenge.
  arXiv  Detail & Related papers  (2025-11-19T02:45:31Z)
• Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement [89.99237142387655]
  We introduce LH-VAE, which enhances semantic robustness through visual semantic constraints and progressive degradations.<n>Latent Harmony is a two-stage framework that redefines VAEs for UHD restoration by jointly regularizing the latent space and enforcing high-frequency-aware reconstruction.<n>Experiments show Latent Harmony achieves state-of-the-art performance across UHD and standard-resolution tasks, effectively balancing efficiency, perceptual quality, and reconstruction accuracy.
  arXiv  Detail & Related papers  (2025-10-09T08:54:26Z)
• Frequency Domain-Based Diffusion Model for Unpaired Image Dehazing [92.61216319417208]
  We propose a novel frequency domain-based diffusion model, named ours, for fully exploiting the beneficial knowledge in unpaired clear data.<n>Inspired by the strong generative ability shown by Diffusion Models (DMs), we tackle the dehazing task from the perspective of frequency domain reconstruction.
  arXiv  Detail & Related papers  (2025-07-02T01:22:46Z)
• UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space [46.43409853027655]
  Diffusion models have shown great potential in generating realistic image detail.<n>Adapting these models to video super-resolution (VSR) remains challenging due to their inherentity and lack of temporal modeling.<n>We propose UltraVSR, a novel framework that enables ultra-realistic and temporally-coherent VSR through an efficient one-step diffusion space.
  arXiv  Detail & Related papers  (2025-05-26T13:19:27Z)
• Freqformer: Image-Demoiréing Transformer via Efficient Frequency Decomposition [83.40450475728792]
  We present Freqformer, a Transformer-based framework specifically designed for image demoir'eing through targeted frequency separation.<n>Our method performs an effective frequency decomposition that explicitly splits moir'e patterns into high-frequency spatially-localized textures and low-frequency scale-robust color distortions.<n>Experiments on various demoir'eing benchmarks demonstrate that Freqformer achieves state-of-the-art performance with a compact model size.
  arXiv  Detail & Related papers  (2025-05-25T12:23:10Z)
• Mixed-granularity Implicit Representation for Continuous Hyperspectral Compressive Reconstruction [16.975538181162616]
  This study introduces a novel method using implicit neural representation for continuous hyperspectral image reconstruction.<n>By leveraging implicit neural representations, the MGIR framework enables reconstruction at any desired spatial-spectral resolution.
  arXiv  Detail & Related papers  (2025-03-17T03:37:42Z)
• Dual-domain Modulation Network for Lightweight Image Super-Resolution [26.992373105057684]
  Lightweight image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution images under limited computational costs.<n>Existing frequency-based SR methods cannot balance the reconstruction of overall structures and high-frequency parts.<n>We show that introducing both wavelet and Fourier information allows our model to consider both high-frequency features and overall SR structure reconstruction while reducing costs.
  arXiv  Detail & Related papers  (2025-03-13T04:59:46Z)
• Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images [64.80875911446937]
  We propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images.<n>For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module.<n>For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module.
  arXiv  Detail & Related papers  (2025-01-02T15:14:40Z)
• Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution [54.293362972473595]
  Image super-resolution (SR) aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts. Current approaches to address SR tasks are either dedicated to extracting RGB image features or assuming similar degradation patterns. We propose a Contourlet refinement gate framework to restore infrared modal-specific features while preserving spectral distribution fidelity.
  arXiv  Detail & Related papers  (2024-11-19T14:24:03Z)
• FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss [5.349799154834945]
  This paper introduces Frequency Consistency for Implicit Neural Representation (FreqINR), an innovative Arbitrary-scale Super-resolution method. During training, we employ Adaptive Discrete Cosine Transform Frequency Loss (ADFL) to minimize the frequency gap between HR and ground-truth images. During inference, we extend the receptive field to preserve spectral coherence between low-resolution (LR) and ground-truth images.
  arXiv  Detail & Related papers  (2024-08-25T03:53:17Z)
• Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration [66.68541690283068]
  We propose a unified paradigm combining the spatial and spectral properties for hyperspectral image restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity. Experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority.
  arXiv  Detail & Related papers  (2020-10-24T15:53:56Z)

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.