Dual-Path Learning based on Frequency Structural Decoupling and Regional-Aware Fusion for Low-Light Image Super-Resolution

• URL: http://arxiv.org/abs/2603.27301v1
• Date: Sat, 28 Mar 2026 15:15:08 GMT
• Title: Dual-Path Learning based on Frequency Structural Decoupling and Regional-Aware Fusion for Low-Light Image Super-Resolution
• Abstract summary: Low-light image super-resolution (LLISR) is essential for restoring fine visual details and perceptual quality under insufficient illumination conditions.<n>We propose Decoupling then Perceive (DTP), a novel frequency-aware framework that separates luminance and texture into semantically independent components.<n>Experiments on the most widely used LLISR benchmarks demonstrate the superiority of our DTP framework.
• Score: 15.557346345104307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-light image super-resolution (LLISR) is essential for restoring fine visual details and perceptual quality under insufficient illumination conditions with ubiquitous low-resolution devices. Although pioneer methods achieve high performance on single tasks, they solve both tasks in a serial manner, which inevitably leads to artifact amplification, texture suppression, and structural degradation. To address this, we propose Decoupling then Perceive (DTP), a novel frequency-aware framework that explicitly separates luminance and texture into semantically independent components, enabling specialized modeling and coherent reconstruction. Specifically, to adaptively separate the input into low-frequency luminance and high-frequency texture subspaces, we propose a Frequency-aware Structural Decoupling (FSD) mechanism, which lays a solid foundation for targeted representation learning and reconstruction. Based on the decoupled representation, a Semantics-specific Dual-path Representation (SDR) learning strategy that performs targeted enhancement and reconstruction for each frequency component is further designed, facilitating robust luminance adjustment and fine-grained texture recovery. To promote structural consistency and perceptual alignment in the reconstructed output, building upon this dual-path modeling, we further introduce a Cross-frequency Semantic Recomposition (CSR) module that selectively integrates the decoupled representations. Extensive experiments on the most widely used LLISR benchmarks demonstrate the superiority of our DTP framework, improving $+$1.6\% PSNR, $+$9.6\% SSIM, and $-$48\% LPIPS compared to the most state-of-the-art (SOTA) algorithm. Codes are released at https://github.com/JXVision/DTP.

Related papers

• Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning [33.97020349697987]
  We propose RNSIDNet, a novel forensic framework that achieves robust detection through enhanced RGB-Noise representation learning.<n>Specifically, our method employs a dual-branch architecture where global RGB semantics, extracted by an attention-refined CLIP backbone, dynamically modulate high-frequency noise artifacts captured by Bayar convolutions via a Feature-wise Linear Modulation (FiLM) module.<n>To further enhance the learned representations, we design a Hard Sample-aware Contrastive Learning (HSCL) strategy.
  arXiv  Detail & Related papers  (2026-07-07T14:52:22Z)
• Lighting-aware Unified Model for Instance Segmentation [12.511503644139369]
  We develop an adapter module that enhances segmentation robustness without fine-tuning the heavy backbone.<n>lca employs a dual-branch architecture to process RGB features alongside contrast maps, enabling physically motivated sensitivity to structural changes.<n>We present a novel Unity-based synthetic dataset specifically designed to accurately replicate complex real-world lighting conditions.
  arXiv  Detail & Related papers  (2026-05-19T19:35:51Z)
• UHD Low-Light Image Enhancement via Real-Time Enhancement Methods with Clifford Information Fusion [51.51707075741303]
  We propose a novel real-time UHD low-light enhancement network based on geometric feature fusion.<n>Our method achieves millisecond-level inference for 4K/8K images on a single consumer-grade device.
  arXiv  Detail & Related papers  (2026-04-10T13:47:04Z)
• Manifold-aware Representation Learning for Degradation-agnostic Image Restoration [135.90908995927194]
  Image Restoration (IR) aims to recover high quality images from degraded inputs affected by various corruptions such as noise, blur, haze, rain, and low light conditions.<n>We present MIRAGE, a unified framework for all in one IR that explicitly decomposes the input feature space into three semantically aligned parallel branches.<n>This modular decomposition significantly improves generalization and efficiency across diverse degradations.
  arXiv  Detail & Related papers  (2025-05-24T12:52:10Z)
• UnfoldIR: Rethinking Deep Unfolding Network in Illumination Degradation Image Restoration [33.290565892897824]
  Deep unfolding networks (DUNs) are widely employed in illumination degradation image restoration (IDIR)<n>We propose a novel DUN-based method, UnfoldIR, for IDIR tasks.<n>We unfold the iterative optimized solution of this model into a multistage network, with each stage comprising a reflectance-assisted illumination correction (RAIC) module and an illumination-guided reflectance enhancement (IGRE) module.
  arXiv  Detail & Related papers  (2025-05-10T16:13:01Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [92.4205087439928]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose the Self-supervised Transfer (PST) and the FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models, effectively mitigating data scarcity.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.<n>This combined approach enables FUSE to construct a universal image-event that only requires lightweight decoder adaptation for target datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains [0.0]
  Low-light image enhancement (LLE) aims to improve the visual quality of images captured in poorly lit conditions.<n>These issues hinder the performance of computer vision tasks such as object detection, facial recognition, and autonomous driving.<n>We propose the Dual Light Enhance Network (DLEN), a novel architecture that incorporates two distinct attention mechanisms.
  arXiv  Detail & Related papers  (2025-01-21T15:58:16Z)
• RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering [26.988572852463815]
  In this paper, we propose a novel end-to-end relightable neural inverse rendering system. Our algorithm achieves state-of-the-art performance in inverse rendering and relighting. Our experiments demonstrate that our algorithm achieves state-of-the-art performance in inverse rendering and relighting.
  arXiv  Detail & Related papers  (2024-09-30T09:42:10Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition [52.89441679581216]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise.<n>We present an innovative video decomposition strategy that incorporates view-independent and view-dependent components.<n>Our framework consistently outperforms existing methods, establishing a new SOTA performance.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
  This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
  arXiv  Detail & Related papers  (2023-08-10T07:53:06Z)
• Learning Detail-Structure Alternative Optimization for Blind Super-Resolution [69.11604249813304]
  We propose an effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without blur kernel prior incorporation for blind SR. In our DSSR, a detail-structure modulation module (DSMM) is built to exploit the interaction and collaboration of image details and structures. Our method achieves the state-of-the-art against existing methods.
  arXiv  Detail & Related papers  (2022-12-03T14:44:17Z)
• Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement [58.72667941107544]
  A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces. We develop a new context-sensitive decomposition network architecture to exploit the scene-level contextual dependencies on spatial scales. We develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels.
  arXiv  Detail & Related papers  (2021-12-09T06:25:30Z)

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.