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cs.CV2025
Multi-Order Matching Network for Alignment-Free Depth Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Yuan Wu +3
Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-quality depth reconstruction. However,…
cs.CV2025
Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
Sichen Guo, Wenjie Li, Yuanyang Liu +3
Recently, Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity, addressing the quadratic computational…
cs.CV2025
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution
Siyu Xu, Wenjie Li, Guangwei Gao +3
Face super-resolution (FSR) under limited computational budgets remains challenging. Existing methods often treat all facial pixels equally, leading to suboptimal resource allocati…