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From the 2 of 33 linked papers with an AI index.

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20242026
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Showing 2025 · cs.CVShow all

14 papers · 2 filters

cs.CV2025

Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration

Gang Wu, Junjun Jiang, Kui Jiang +2

All-in-One Image Restoration (AiOIR), which addresses diverse degradation types with a unified model, presents significant challenges in designing task-aware prompts that effective…

cs.CV2025

DSwinIR: Rethinking Window-based Attention for Image Restoration

Gang Wu, Junjun Jiang, Kui Jiang +2

Image restoration has witnessed significant advancements with the development of deep learning models. Transformer-based models, particularly those using window-based self-attentio…

cs.CV2025

Semantics and Content Matter: Towards Multi-Prior Hierarchical Mamba for Image Deraining

Zhaocheng Yu, Kui Jiang, Junjun Jiang +3

Rain significantly degrades the performance of computer vision systems, particularly in applications like autonomous driving and video surveillance. While existing deraining method…

cs.CV2025

Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation

Daxin Li, Yuanchao Bai, Kai Wang +3

Autoregressive (AR) models, the theoretical performance benchmark for learned lossless image compression, are often dismissed as impractical due to prohibitive computational cost.…

cs.CV2025

FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion

Pihai Sun, Junjun Jiang, Yuanqi Yao +4

Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability stemming from two factors: 1) limited an…

cs.CV2025

SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance

Yuru Xiao, Xianming Liu, Deming Zhai +3

Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints. However, its effectiveness is hampered when working with sparsely available views…