AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A frequency–spatial collaborative enhanced super-resolution network, termed FSCNet, is proposed, which enables fine-grained reconstruction of pore microstructures with a lightweight architecture, validating its effectiveness for digital rock image super-resolution reconstruction.
A robust attention-guided multi-dimensional feature fusion network (LFAMF) for LF angular reconstruction that significantly outperforms state-of-the-art methods, particularly in maintaining structural integrity at occlusion boundaries and highly textured areas while ensuring superior angular consistency.
A lightweight dual-dimension modulation aggregation network, which combines channel-wise and spatial feature interactions to achieve more accurate reconstruction, and shows that DMANet achieves competitive reconstruction performance with lower model complexity and runtime overhead.
Single-image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) observations while preserving structural information and high-frequency detail. Although the Hybrid Multi-Axis Network (HMA) effectively combines local and nonlocal attention, its shallow input representation still mixes low-frequency structure, directional detail, and noise-like high-frequency components. This study investigates whether an explicit frequency prior can be introduced before the HMA backbone without substantially increasing computational cost. Two discrete-wavelet-transform front-ends are examined under the ×2 setting. HMA-WSB uses lightweight subband-specific processing and weighted fusion before a shared HMA backbone, whereas HMA-MSB introduces asymmetric multi-subband branches and cross-band fusion. Evaluation includes the reported external 15-image experiment, selected-image pilots from Set5, Set14, BSD100, and Urban100, and supplementary medical and texture-domain samples. The results show small, content-dependent differences rather than a consistent reconstruction advantage: the proposed variants are slightly favorable on several images containing dense multidirectional detail, but the original HMA remains stronger on other natural, medical, and periodic-texture samples. Computational analysis on an NVIDIA GeForce RTX 5070 with a 64×64 low-resolution input shows that HMA-WSB increases measured inference latency by 1.637% with negligible parameter and memory overhead. HMA-MSB increases latency by 5.078%, parameter count by 1.463%, and estimated FLOPs by 0.489%. These findings indicate that wavelet-guided subband processing is compatible with HMA and that WSB provides the more computationally economical extension. However, because the standard-dataset evaluation is based on selected images and a complete component-level ablation is not available, the results should be interpreted as preliminary evidence of a content-dependent quality-cost trade-off rather than proof of broad superiority.
The results indicate that the collaboration between global semantics and local details within a unified weighting domain can effectively improve the separability and deploy ability of high-resolution segmentation.
Comprehensive evaluations on multiple datasets and SR scales indicate that the SVRCL-SR achieves superior performance in artifact suppression and high-frequency detail restoration, along with strong robustness.
Remote sensing image super-resolution (RSISR) provides an effective means of improving spatial detail for Earth observation and satellite image interpretation. However, existing methods often rely on increasingly complex network designs with deeper hierarchies and expanded channel capacities to pursue higher performance, resulting in heavy models with high computational cost, which restricts their deployment on resource-constrained platforms. To address this challenge, we propose a novel reparameterized feature enhancement network (RepFEN) for lightweight and accurate RSISR tasks. Specifically, a multi-scale reparameterized module (MRepM) is designed to capture multi-scale spatial information and enhance texture representation. Furthermore, a partial-channel gated attention module (PCGAM) is introduced to selectively enhance discriminative features along the channel dimension, effectively improving fine-grained detail restoration. By integrating structural reparameterization and multi-scale lightweight modules, the proposed method achieves a better balance between reconstruction accuracy and inference efficiency. Extensive experiments on both remote sensing and natural image super-resolution benchmarks demonstrate that our method achieves superior performance compared to existing state-of-the-art methods, while maintaining minimal computational overhead, showing significant potential for real-world applications.
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