A Unified Resolution-Conditioned Framework for Orthogonal Line-Scanning Image Fusion

• URL: http://arxiv.org/abs/2608.03107v1
• Date: Tue, 04 Aug 2026 04:28:39 GMT
• Title: A Unified Resolution-Conditioned Framework for Orthogonal Line-Scanning Image Fusion
• Abstract summary: We present a resolution-conditioned fusion framework based on Rank Enhanced Linear Attention (RELA)<n>We also introduce Adaptive RELA, which replaces fixed- kernel rank enhancement with ratio-conditioned multi-scale depthwise convolutions.<n>The resulting model achieves 34-40 dB PSNR across configurations, whereas unconditioned multi-slit training collapses to 24.3 dB and per-slit specialists lose 4-9 dB outside their training setting.
• Score: 5.000212382506099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Laser line-scanning microscopy enables fast volumetric imaging but produces anisotropic lateral resolution. Orthogonal line scans provide complementary directional information that can recover near-isotropic resolution, yet existing deep-learning methods require a separate model for each optical configuration. We present a unified, resolution-conditioned fusion framework based on Rank Enhanced Linear Attention (RELA). Feature-wise Linear Modulation (FiLM) conditions the network continuously on the resolving-power ratio, enabling one model to adapt across slit widths. We further introduce Adaptive RELA, which replaces fixed-kernel rank enhancement with ratio-conditioned multi-scale depthwise convolutions and uses a learnable attention temperature to adjust selectivity with degradation severity. Training data spanning multiple slit configurations are generated using a physics-grounded separable point-spread-function model verified against measured optical data at 48.3 dB accuracy. The resulting model achieves 34-40 dB PSNR across configurations, whereas unconditioned multi-slit training collapses to 24.3 dB and per-slit specialists lose 4-9 dB outside their training setting. It also generalizes smoothly to unseen intermediate configurations without interpolation artifacts. Ablations show that FiLM resolves configuration ambiguity, global linear attention captures long-range directional correspondences, and adaptive temperature yields an additional 2 dB in the challenging near-isotropic regime, where complementary signals are weak.

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