RASLF: Representation-Aware State Space Model for Light Field Super-Resolution

• URL: http://arxiv.org/abs/2603.16243v1
• Date: Tue, 17 Mar 2026 08:28:00 GMT
• Title: RASLF: Representation-Aware State Space Model for Light Field Super-Resolution
• Abstract summary: RASLF is a representation-aware state-space framework that explicitly models structural correlations across multiple LF representations.<n>We introduce a Representation Aware Asymmetric Scanning mechanism that dynamically adjusts scanning paths based on the physical properties of different representation spaces.<n>Experiments on various public benchmarks show that RASLF achieves the highest reconstruction accuracy while remaining highly computationally efficient.
• Score: 16.453425321865762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current SSM-based light field super-resolution (LFSR) methods often fail to fully leverage the complementarity among various LF representations, leading to the loss of fine textures and geometric misalignments across views. To address these issues, we propose RASLF, a representation-aware state-space framework that explicitly models structural correlations across multiple LF representations. Specifically, a Progressive Geometric Refinement (PGR) block is created that uses a panoramic epipolar representation to explicitly encode multi-view parallax differences, thereby enabling integration across different LF representations. Furthermore, we introduce a Representation Aware Asymmetric Scanning (RAAS) mechanism that dynamically adjusts scanning paths based on the physical properties of different representation spaces, optimizing the balance between performance and efficiency through path pruning. Additionally, a Dual-Anchor Aggregation (DAA) module improves hierarchical feature flow, reducing redundant deeplayer features and prioritizing important reconstruction information. Experiments on various public benchmarks show that RASLF achieves the highest reconstruction accuracy while remaining highly computationally efficient.

Related papers

• SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models [31.77829987533264]
  We propose a novel unified multimodal framework featuring textbfSemantic-textbfPixel self-alignment and textbfRouting (textbfSPAR)<n>To reconcile semantic perception with pixel-level reconstruction, we introduce an asymmetric dual-stream unified tokenizer. A lightweight semantic stream anchors discriminative features, while a Transformer-augmented pixel stream recovers fine-grained visual details into a unified compact latent space.
  arXiv  Detail & Related papers  (2026-06-22T08:48:19Z)
• Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering [57.38215918201251]
  We propose HERL, a Hyperbolic Enhanced Representation Learning framework for Incomplete Multi-View Clustering.<n> operating within the Poincaré ball, HERL constructs a structure-aware latent space to enhance representation learning.<n>We show that HERL consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-04-18T10:50:46Z)
• Efficient Image Super-Resolution with Multi-Scale Spatial Adaptive Attention Networks [3.4782736103257323]
  This paper introduces a lightweight image super-resolution (SR) network, termed the Multi-scale Spatial Adaptive Attention Network (MSAAN)<n>The core of our approach is a novel Multi-scale Spatial Adaptive Attention Module (MSAA), designed to jointly model fine-grained local details and long-range contextual dependencies.
  arXiv  Detail & Related papers  (2026-02-22T07:47:39Z)
• CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval [54.15776146365823]
  Composed Image Retrieval (CIR) enables users to search for target images using both a reference image and manipulation text.<n>We propose CSMCIR, a unified representation framework that achieves efficient query-target alignment through three synergistic components.
  arXiv  Detail & Related papers  (2026-01-07T09:21:38Z)
• 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)
• Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity [42.8098014428052]
  Hyperspectral image (HSI) representation is fundamentally challenged by pervasive non-uniformity.<n>We propose FairHyp, a fairness-directed framework that disentangles and resolves the threefold non-uniformity.<n>Our findings redefine fairness as a structural necessity in HSI modeling and offer a new paradigm for balancing adaptability, efficiency, and fidelity.
  arXiv  Detail & Related papers  (2025-05-16T14:00:11Z)
• Diffusion-based Light Field Synthesis [50.24624071354433]
  LFdiff is a diffusion-based generative framework tailored for LF synthesis. We propose DistgUnet, a disentanglement-based noise estimation network, to harness comprehensive LF representations. Extensive experiments demonstrate that LFdiff excels in synthesizing visually pleasing and disparity-controllable light fields.
  arXiv  Detail & Related papers  (2024-02-01T13:13:16Z)
• 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)
• Deep Diversity-Enhanced Feature Representation of Hyperspectral Images [87.47202258194719]
  We rectify 3D convolution by modifying its topology to enhance the rank upper-bound. We also propose a novel diversity-aware regularization (DA-Reg) term that acts on the feature maps to maximize independence among elements. To demonstrate the superiority of the proposed Re$3$-ConvSet and DA-Reg, we apply them to various HS image processing and analysis tasks.
  arXiv  Detail & Related papers  (2023-01-15T16:19:18Z)
• A Hierarchical Coding Scheme for Glasses-free 3D Displays Based on Scalable Hybrid Layered Representation of Real-World Light Fields [0.6091702876917279]
  Scheme learns stacked multiplicative layers from subsets of light field views determined from different scanning orders. The spatial correlation in layer patterns is exploited with varying low ranks in factorization derived from singular value decomposition on a Krylov subspace. encoding with HEVC efficiently removes intra-view and inter-view correlation in low-rank approximated layers.
  arXiv  Detail & Related papers  (2021-04-19T15:09:21Z)

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.